Machine Learning Jobs

NVIDIAHQI 89

For more than 25 years, NVIDIA has been driving innovation in computer graphics, PC gaming, and accelerated computing. It’s a distinctive heritage of creativity driven by excellent technology—and outstanding people. Today, we’re harnessing the boundless capabilities of AI to build the next era of computing. An era where our GPU serves as the intelligence behind computers, robots, and autonomous vehicles that perceive the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be part of a varied, encouraging environment where everyone is motivated to perform at their highest level. Join our team and discover how you can build a lasting impact on the world. NVIDIA's Metropolis team advances physical AI by building intelligent video analytics and perception solutions for smart cities, industrial automation, and autonomous systems at scale. We seek a Senior ML Engineer to compose and deliver next-generation Metropolis solutions powered by Cosmos, NVIDIA's world foundation model platform. Your main task will be constructing production-quality AI capabilities for Metropolis by demonstrating Cosmos to address real-world perception and physical AI challenges. You will also partner closely with the Cosmos team to develop and broaden the platform’s features aligned with the Metropolis product roadmap. If you are driven to push generative AI, simulation, and large-scale model development forward and want your work to impact real-world systems, we invite you to apply. What you'll be doing: Build and deliver innovative Metropolis AI solutions powered by Cosmos world foundation models, addressing real-world requirements in intelligent video analytics, perception, and physical AI. Find areas where Cosmos models underperform or lack capability and recommend new solutions. These involve generating synthetic data, refining tuning methods, and improving architecture. Metropolis use cases serve as the main reference. Partner with the Cosmos team to identify and prioritize new platform features guided by the Metropolis product roadmap, contributing to deliverables that support the wider ecosystem. Lead the open-sourcing of solutions and research artifacts developed by the team, contributing to the broader AI and research community. Stay current with the latest advances in foundation models, generative architectures, and training methodologies, and actively bring relevant insights back to the team. Partner multi-functionally with Product, Program, Engineering, and Data Procurement teams to drive alignment and unblock execution. What we need to see: MSc or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience. 8+ years of proven experience in applied machine learning or AI research. Deep expertise in deep learning fundamentals, with hands-on experience in diffusion models and generative architectures. Experience in pre-training or refining large language models (LLMs), vision-language models (VLMs), or world foundation models (WFMs). Experience working with large-scale foundation models, including training workflows, fine-tuning techniques, and evaluation approaches. Experience working with simulation environments like Isaac Sim or similar platforms. Consistent track record of leading projects end-to-end and delivering results with clarity and accountability. Ability to manage and complete tasks across multiple parallel workstreams in a fast-paced, evolving environment. End-to-end understanding of ML development and deployment life cycle with the ability to quickly adopt and bring to bear modern AI development tools and workflows. Ways to stand out from the crowd: Hands-on experience with model compression, quantization, and real-time inference optimization for production deployments. Previous experience implementing AI solutions in physical settings such as public areas, smart infrastructure, or robotics platforms. Experience scaling AI systems across distributed infrastructure, including multi-node training and large-scale data pipelines. Published research or open-source contributions in relevant areas such as generative models, synthetic data, or physical AI. Familiarity with CUDA, Triton, or low-level GPU kernel development for inference pipeline acceleration. With a competitive salary package and benefits, NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. Are you a creative and autonomous Systems Software Engineer who loves challenges? Do you have a genuine passion for advancing the state of Computer Vision 3D across a variety of industries? If so, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 11, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.

Högskolan i HalmstadHQI 79

Halmstad University Work at a University where different perspectives meet! Halmstad University adds value, drives innovation and prepares people and society for the future. Since the beginning in 1983, innovation and collaboration with society have characterised the University's education and research. The research is internationally reputable and is largely conducted in a multidisciplinary manner within the University's two focus areas: Health Innovation and Smart Cities and Communities. The University has a wide range of education with many popular study programmes. The campus is modern and well-equipped, and is situated close to both public transportation and the city center. More information about working at Halmstad University The School of Information Technology Halmstad University’s School of Information Technology (ITE) is a renowned multi-disciplinary institution with around 155 employees from 20 different countries. ITE is internationally recognised for its applied research and collaborative initiatives, focusing on smart technology and its practical applications. Within ITE, our students and researchers engage in diverse areas of study, including electronics, AI, information-driven healthcare, autonomous vehicles, social robotics, and digital design. We offer a comprehensive range of educational programmes, ranging from undergraduate to doctoral levels, as well as professional development opportunities. Research is conducted within the University’s research programmes, especially Information Driven Care (IDC), Re-Imagining Future Smart Living – beyond the Living Lab (REBEL), Learning in a Digitalised Society (LeaDS) and the Future Industry Research Programme (FIRP). ITE is home to Leap for Life, an innovation centre for information-driven care, as well as the Electronics Centre in Halmstad (ECH), a collaborative space for electronic development. More information about the School of Information Technology Description Halmstad University has received funding for the ENGADE and UNIFY-AI projects. Both projects address the development of robust machine-learning methods for time-series data, but from complementary perspectives. ENGADE focuses on developing theoretical foundations and new diffusion-based methods for anomaly detection in time series. Its research includes multivariate and multiscale time-series modelling, concept drift, and explainability. UNIFY-AI focuses on developing a time-series foundation model trained on heterogeneous fleet data and adapting it to industrial aftermarket and uptime applications. Its use cases include anomalous brake-wear detection, together with brake-pad wear prediction and energy-storage-system degradation forecasting. UNIFY-AI also studies robustness to missing signals, new operating conditions, and distribution shifts over time. The scientific overlap between the projects concerns robust anomaly detection in heterogeneous and evolving time-series data. A PhD student is needed to investigate how diffusion-based anomaly-detection methods can be combined with representations learned by time-series foundation models. The research will examine whether reusable representations learned from heterogeneous data can support anomaly detection across different operating conditions while remaining robust to missing signals and changes in the data distribution. The PhD student will undertake a coherent research project within this shared area. The student will contribute to the diffusion-based anomaly-detection objectives of ENGADE and to the foundation-model-based anomaly-detection and robustness objectives of UNIFY-AI. This requires hiring a PhD student with a focused research profile in machine learning for time series. Tasks include: - Research on diffusion-based anomaly detection within the time-series foundation-model framework developed in UNIFY-AI. The work will contribute to ENGADE through the development of theoretically grounded diffusion-based methods for anomaly detection and to UNIFY-AI by supporting its anomaly-detection applications on heterogeneous vehicle data. - Publish research findings in peer-reviewed scientific venues - Complete relevant doctoral coursework as part of the doctoral programme - Teaching and supervision duties (up to 20%) can be included Qualifications Only those who are or have been admitted to third-cycle courses and study programs at a higher education may be appointed to doctoral studentships. (The Higher Education Ordinance Chapter 5 Section 3). The student’s ability to benefit from doctoral studies will be taken into account when we make the appointment. (The Higher Education Ordinance Chapter 5 Section 5). - A M.Sc. degree (or equivalent, corresponding to at least 240 ECTS credits including a degree project) in computer science, computer engineering, electrical engineering, Physics, or a closely related field with focus on machine learning and AI. - Good communication skills in English is required. - Strong knowledge of machine learning, artificial intelligence, data mining, or signal processing is required. - Excellent programming skills are required. - Analytical problem solving and organizational abilities are required. Salary Doctoral students are employees of the University and paid a salary according to a uniform salary scale, adjusted in relation to the progress in education. Application Applications should be sent via Halmstad University's recruitment system Varbi (see link on this page). How to design your application General Information We value the qualities that gender balance and diversity bring to our organization. We therefore welcome applicants with different backgrounds, gender, functionality and, not least, life experience. Read more about Halmstad University Information for International Applicants Choosing a career in a foreign country is a big step. Thus, to give you a general idea what we have to offer in terms of benefits and life in general for you and your family/spouse/partner please visit: International staff support

Collective.workHQI 74

Dans le cadre du développement de solutions Data & IA dédiées aux enjeux du retail et de l'expérience en magasin, nous recherchons un-e Senior Data Scientist / Senior Machine Learning Engineer pour rejoindre une équipe Data multidisciplinaire. L'équipe conçoit, développe et maintient des solutions analytiques et d'Intelligence Artificielle à fort impact : prévision du trafic en magasin, moteurs de classification, analyse de la performance, solutions intelligentes à destination des équipes opérationnelles, agents IA et GenAI. Vous évoluerez aux côtés de Data Scientists, Data Analysts, Analytics Engineers et ML Engineers, en collaboration étroite avec les équipes Product, Engineering et les interlocuteurs métiers. VOS PRINCIPALES MISSIONS Vous interviendrez sur l'ensemble du cycle de vie des solutions Machine Learning, avec une forte orientation production, performance et impact métier. Vous serez notamment amené-e à : * Développer, industrialiser et maintenir en conditions opérationnelles des solutions de Machine Learning : pipelines batch de prévision, moteurs de classification et autres modèles appliqués aux problématiques du retail. * Évaluer et améliorer les modèles et méthodologies de prévision existants afin d'optimiser leur précision, leur robustesse et leur efficacité. * Participer à la mise en production et au suivi des modèles dans un environnement Data & Cloud à grande échelle. * Appliquer et faire progresser les standards de développement, de qualité et de gouvernance des données. * Collaborer étroitement avec les équipes métier, Engineering et Product afin de transformer les enjeux opérationnels en problématiques Data/IA concrètes. * Structurer les analyses, interpréter les résultats et formuler des recommandations directement exploitables. * Communiquer les résultats auprès des différentes parties prenantes à travers des visualisations et présentations claires et synthétiques. * Assurer une veille sur les nouvelles approches en Data Science, Machine Learning, forecasting, GenAI et technologies associées Environnement technique: Data & Cloud : Databricks, AWS, GCP Data Science / ML : Python, Spark, PySpark, scikit-learn, TensorFlow, PyTorch Transformation : dbt MLOps : MLflow, Airflow CI/CD : GitHub Actions Serving : Docker, Protobuf, gRPC Documentation & code : Git, Confluence Data Visualisation : Tableau Nous recherchons un profil Senior disposant d'au moins 5 ans d'expérience en Data Science / Machine Learning, capable d'être rapidement autonome sur des problématiques complexes et des solutions déjà en production. Vous disposez d'une solide maîtrise des fondamentaux du Machine Learning, de l'analyse statistique et plus particulièrement de l'analyse et de la prévision de séries temporelles. Une expérience concrète du développement, du déploiement et du maintien en production de solutions Machine Learning est indispensable. Vous maîtrisez notamment Python, SQL, Spark/PySpark, scikit-learn et dbt, ainsi que les environnements Databricks, AWS et/ou GCP. Vous êtes également à l'aise avec Airflow/Astronomer, GitHub Actions et MLflow, ainsi qu'avec les bonnes pratiques de développement logiciel et de CI/CD appliquées à la Data. La connaissance de TensorFlow/PyTorch, Docker, Protobuf, gRPC et Tableau constitue un atout. Une expérience avec les LLM, systèmes d'agents et solutions GenAI sera également appréciée. Au-delà de l'expertise technique, vous savez comprendre les problématiques métier et les traduire en solutions Data concrètes. Une connaissance des enjeux retail est particulièrement pertinente pour cette mission. Autonome, analytique et proactif-ve, vous appréciez les environnements multidisciplinaires et savez collaborer avec des équipes Data, Engineering, Product et métier. Anglais professionnel indispensable.

StripeHQI 91

Who we are About the team The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10+ real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users. The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products like defenses against AI token theft, free trial abuse, and programmatic attacks. What you'll do In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch. Responsibilities Design, build, train, evaluate, deploy, and own ML models in production that detect fraud across Stripe’s global payments network Design and build large-scale ML systems that operate on diverse and large scale data Experiment and iterate on ML models to achieve key business goals around data quality and accuracy Develop pipelines and automated processes to train and evaluate models in offline and online environments Integrate ML models into production systems and ensure their scalability and reliability Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions Mentor engineers and contribute to a strong ML engineering culture within the team Who you are We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 10+ years of industry experience building and shipping ML systems in production Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark Hands-on experience in designing, training, and evaluating machine learning models Hands-on experience in productionizing and deploying models at scale Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets Strong collaboration skills and the ability to work across teams and contribute to peers' success Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset Preferred qualifications MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science) Experience in fintech, open banking, or financial data domains Experience with NLP, LLMs, or text classification at scale Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems Experience with deep learning architectures, including transformers

StripeHQI 91

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Radar ML team builds the fraud detection models that protect Stripe's $1.9 trillion payment network from fraud. The team owns 10 real-time deep learning models that must constantly evolve to stay ahead of fraudsters. Each ML improvement translates directly into dollar impact for Stripe and its users. The team's models also power the Radar product suite that tens of thousands of businesses use to screen payments and manage fraud. Radar is growing fast, and the team is actively building new products to protect against AI token theft, free trial abuse, and scripted attacks. What you’ll do In this role, you will own ML work across the full lifecycle: researching new fraud patterns, building and deploying models, and sharing results directly with top Stripe customers. You will have opportunities to optimize Stripe’s most intensive ML models, and opportunities to ship 0-to-1 products from scratch. Responsibilities Build, train, evaluate, and deploy ML models that detect fraud across Stripe’s global payments network Research emerging fraud patterns like token theft and develop ML solutions to address them Apply advances in deep learning to improve model quality and detection rates at scale Co-build new fraud and abuse products directly with top users Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement. Minimum requirements 2+ years of experience training, evaluating, and deploying ML models in a production environment Proficiency in Python and common data and ML frameworks like SQL, Spark, and PyTorch Strong knowledge of production ML systems; and data analysis, statistics, and experiment design fundamentals Active interest in the latest ML developments, and how they can be leveraged to solve business problems Preferred qualifications Strong software engineering skills and ability to design ML solutions through entire product stack Experience building and optimizing real-time, low-latency ML infrastructure at scale Experience applying ML to fraud detection, integrity, trust and safety, or a closely related domain

CheckrHQI 86

About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud [phone removed] List and is a Y Combinator 2024 Breakthrough Company . About the team/role We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering. Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily. This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk. This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer. We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results. What you’ll do • Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services. • Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together. • Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end. • Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion. • Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect. • Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first. What you bring • A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience • 6+ years building software professionally, with at least 2 of those building ML systems that run in production • Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation • You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on • Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal • You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it • An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes. Nice to have • Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar) • Background in document processing, OCR, or information extraction • Experience with PySpark or large-scale data processing • Ruby experience (Checkr’s platform runs on Rails) • Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech) • Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools Pay Transparency Disclosure We use geographic cost of labor as an input to develop ranges for our roles and as such, each location where we hire may have a different range. If this role is remote, we have listed the top to the bottom of the possible range, but we will specify the target range for an exact location when you are selected for a recruiting discussion. For more information on our compensation philosophy, see our website . On-target Earnings OR Base Salary range (San Francisco, CA) $207,000 — $244,000 USD On-target Earnings OR Base Salary range (Denver, CO) $176,000 — $207,000 USD What We Offer • A fast-paced and collaborative environment • Learning and development allowance • Competitive cash and equity compensation, and opportunity for advancement • 100% medical, dental, and vision coverage • Up to $25K reimbursement for fertility, adoption, and parental planning services • Flexible PTO policy • Monthly wellness stipend At Checkr, we believe an in office work environment strengthens collaboration, drives innovation, and encourages connection. Our hub locations are Denver, CO; San Francisco, CA; Nashville, TN; and Santiago, Chile. Individuals are expected to work from the office 3+ days a week. In-office perks are provided, such as lunch five times a week, a commuter stipend, and an abundance of snacks and beverages. A relocation stipend may be available for those willing to relocate to a Checkr hub location. Equal Employment Opportunities at Checkr Checkr is committed to building the best product and company, which requires hiring talented and qualified individuals with a diverse set of perspectives and lived experiences. Checkr believes in hiring people of all backgrounds, including those whose histories are impacted by the justice system in accordance with local, state, and/or federal laws, including the San Francisco’s Fair Chance Ordinance . Applicant Privacy Policy If you are a California resident or are located in Alberta or British Columbia, our Applicant Privacy Policy applies to our collection and processing of your personal information when you apply for a role with us or otherwise participate in our recruitment process. For applicants in Massachusetts: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. *Legitimate Checkr emails will always include our official domain name after the @ symbol (e.g., [contact via application link] or [contact via application link] ).

clickhouseHQI 86

Recognised on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists. WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f[phone removed]d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

DeliverooHQI 89

Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We're looking for a Senior Machine Learning Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office). This role sits in our Consumer Org, where you'll work on recommendation problems with direct, measurable impact on how customers discover relevant content. You’ll collaborate with experienced scientists and engineers across DoorDash, Deliveroo and Wolt, learning from multiple recommendation systems while helping shape the next generation of the experience. What You'll Be Doing Design, develop and improve recommendation, ranking and retrieval models that surface relevant restaurants, dishes, items and content to customers. Own applied ML problems end to end: frame the problem, analyze data, develop models, define offline evaluation, run experiments and monitor production performance. Develop methods that balance relevance with product and customer needs, such as diversity, availability, business constraints and changing user intent. Collaborate closely with Software Engineers, ML Engineers, Product Managers and Analysts to turn scientific insights into reliable customer-facing products. Evaluate and apply state-of-the-art ML methods where they meaningfully improve recommendation quality, robustness or efficiency. Contribute to a high bar for applied-science practice through technical reviews, knowledge sharing and thoughtful experimentation. What You'll Need to Thrive You have substantial hands-on experience applying machine learning to real-world problems and a track record of bringing models from development into production; a PhD with relevant applied research experience is equally welcome. You have experience with recommendation systems, ranking, retrieval, personalisation, or closely related ML problems. You can independently turn an ambiguous customer or product problem into a well-scoped ML approach, make sound trade-offs and drive it to a measurable outcome. You are proficient in Python and experienced with modern ML frameworks and large-scale data processing. You understand how to evaluate ML systems rigorously, including offline metrics, experiment design and interpreting online results. You communicate complex technical ideas clearly and work effectively with cross-functional partners. Why Join Us? At Deliveroo, you'll do work that matters—solving real-world problems in a three-sided marketplace that's constantly evolving. We're food lovers, problem solvers, community builders and more, brought together by a shared drive to make things better. Working here you can expect to: 🔧 Solve meaningful problems at real scale Work on a complex, always-on marketplace that impacts millions every day. 🌱 See your impact, fast Ship, test and improve ideas quickly in a low-hierarchy, high-ownership environment. 🧠 Grow through challenge and ownership Take on big, ambiguous problems and accelerate your career with strong support. 🌎 A culture built for builders High standards, collaboration, flexible working and continuous learning. 💰 Share in the success you help create Competitive salary and equity options, so you're rewarded for the impact you make. ➡️ Want a deeper look at how we build? Check out our Tech Blog. Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

clickhouseHQI 86

Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads. The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla. We’re on a mission to transform how companies use data. Come be a part of our journey! ABOUT THE TEAM The AI/ML Engineering team builds and operates ClickHouse's AI and machine learning products end-to-end. This includes the Agentic Data Stack, AI Functions, chDB, the in-Console copilot, and the AI/ML partnerships that distribute them — together with the shared components and expertise that let every other ClickHouse team ship AI in the surfaces they own. Our team is looking for highly skilled and experienced software engineers to join us, who will be responsible for designing, building, and operating the products that make ClickHouse the platform of choice for agents and data scientists. WHAT YOU WILL DO - Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform. - API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services. - Ecosystem Integrations: Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools. - Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation. - Oncall: Participate in the daytime oncall rotation along with the rest of the team. - ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products. WHAT YOU WILL BRING ALONG - 5+ years of software engineering experience in production environments - Exposure to working directly with AI/ML technologies - Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases - A high level of ownership and can drive features from concept to production with minimal supervision - Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders - Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions Nice to have - Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment - Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus - Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f[phone removed]d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

ServiceNowHQI 77

What you get to do in this role: Contribute to the design, development and implementation of the core Voice Platform (Audio Acquisition, Conversation Management, Speech Generation, integration with API and self-hosted Voice AI models, Voice Harness)  Partner with product managers to work with customers, understand their use cases and technology challenges, and build for their success  Build and deploy large Voice AI distributed systems at scale spanning multiple regions and datacenters  Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.)  Contribute to the continuous improvement of the SRE practice by turning operational use cases into requirements for software tooling  Be a mentor for colleagues and help promote knowledge-sharing across Voice AI engineering disciplines  To be successful in this role you have: 12+ year of relevant industry experience  Hands-on experience building distributed Voice AI systems with a large operational scale  4+ years building LLM-based applications (integrating models, not training)  4+ years of development experience with Python, Golang, Java, or similar languages.   2+ years of experience building agents (prompt engineering) and developing LLM-based experiences  Working experience operating highly available distributed workloads on Kubernetes following a DevOps approach.   Experience building software for compliance and security in regulated environments is considered an asset   Critically thinking around AI integrations for work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.    Experience building with AI tools such as Claude Code, Cursor, Windsurf  For positions in this location, we offer a base pay of $201,300 - $352,300 , plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity,  veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.   Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.  Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.  From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

ServiceNowHQI 77

What you get to do in this role: Contribute to the design, development and implementation of the core Voice Platform (Audio Acquisition, Conversation Management, Speech Generation, integration with API and self-hosted Voice AI models, Voice Harness)  Partner with product managers to work with customers, understand their use cases and technology challenges, and build for their success  Build and deploy large Voice AI distributed systems at scale spanning multiple regions and datacenters  Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.)  Contribute to the continuous improvement of the SRE practice by turning operational use cases into requirements for software tooling  Be a mentor for colleagues and help promote knowledge-sharing across Voice AI engineering disciplines  To be successful in this role you have: 8+ year of relevant industry experience  Hands-on experience building distributed Voice AI systems with a large operational scale  4+ years building LLM-based applications (integrating models, not training)  4+ years of development experience with Python, Golang, Java, or similar languages.   2+ years of experience building agents (prompt engineering) and developing LLM-based experiences  Working experience operating highly available distributed workloads on Kubernetes following a DevOps approach.   Experience building software for compliance and security in regulated environments is considered an asset   Critically thinking around AI integrations for work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.    Experience building with AI tools such as Claude Code, Cursor, Windsurf    For positions in this location, we offer a base pay of $176,100 - $308,200 , plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity,  veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.   Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.  Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.  From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

PinterestHQI 93

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . Pinterest is a visual discovery platform where hundreds of millions of people come to find inspiration and decide what to try, buy, or do next. Our Ads Quality organization builds the machine-learning systems that make ads relevant and valuable to Pinners while delivering meaningful outcomes for advertisers. We are seeking a Director of Machine Learning Engineering to lead a broad portfolio of Ads Quality modeling teams focused on engagement, conversion, ROAS optimization, ranking, representation learning, and ML-powered experimentation. In this role, you will shape and drive a unified technical strategy across Ads Quality, leading teams responsible for engagement ranking, oCPM and conversion modeling, ROAS optimization, lightweight ranking and retrieval models, foundation model adoption, sequence and multimodal modeling, and the quality and efficiency of production machine learning systems. What you’ll do: Set the technical vision and multi-year strategy for Ads Quality machine learning, connecting model innovation to Pinner value, advertiser performance, revenue, and marketplace health. Lead and develop a group of engineering managers, senior technical leaders, and machine-learning engineers across multiple modeling domains. Establish a coherent modeling roadmap across engagement, conversion, ROAS, relevance, ranking, and foundation-model initiatives. Drive improvements in model quality, calibration, generalization, cold-start performance, attribution, and robustness across Pinterest surfaces. Guide the evolution of Ads models toward larger, more generalizable architectures, including foundation models, distillation, long-context sequence modeling, multimodal representations, and cross-domain learning. Ensure that modeling investments translate into reliable production outcomes through strong offline evaluation, online experimentation, launch discipline, and post-launch monitoring. Partner closely with Ads Product, Ads Data Science, Ads Signals, Ads Retrieval, Ads Delivery, Measurement, Core, ATG, and ML Infrastructure. Set expectations for training-serving parity, data quality, privacy, reliability, latency, capacity, and cost efficiency. Improve engineering velocity through better experimentation workflows, reusable modeling infrastructure, automation, and agentic development tools. Build a culture of technical excellence, candid collaboration, inclusion, ownership, and continuous learning. Represent Ads Quality ML in senior leadership forums and communicate strategy, tradeoffs, risks, and results clearly to technical and non-technical audiences. What we’re looking for: Minimum 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations. Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams. Strong understanding of modern deep-learning and recommender-system techniques, including sequence models, embeddings, multimodal models, multi-task learning, foundation models, distillation, and reinforcement learning. Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems is strongly preferred. Proven ability to connect modeling objectives and offline metrics to online experiments and business outcomes. Experience operating production ML systems with demanding requirements for latency, availability, calibration, privacy, reliability, and cost. Strong judgment in balancing near-term product delivery with foundational technical investments. Track record of building high-performing organizations, developing senior leaders, and creating effective operating mechanisms. Excellent communication and collaboration skills, with the ability to influence across organizational boundaries. Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience; advanced degree preferred. Relocation Statement : This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. In-Office Requirement Statement : We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. This role will need to be in the office for in-person collaboration 1-2x per week and therefore needs to be in a commutable distance from one of the following offices: Palo Alto, San Francisco. #LI-SM4 #LI-HYBRID At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here . US based applicants only $314,580 — $550,515 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support. By submitting this application, I certify that all information submitted in my application and throughout the hiring process is true, accurate, and complete to the best of my knowledge. I understand that any false statement, omission, or misrepresentation may disqualify me from employment consideration or result in termination if discovered after hire.

Tradeindia.com - Infocom NetworkHQI 77

Tradeindia is looking for Machine Learning Engineers to join its engineering team. The ideal candidate will have industry experience working on a recommendation engine, a range of classification and optimization problems e.g.: Collaborative filtering/recommendation User journey prediction Search ranking Text/ sentiment classification or spam detection Predictive analytics The position will involve taking Algorithm and Programming skills and applying them to some of the most exciting and massive SMEs user data. Qualification: M.tech, B.tech, M.C.A. Location: New Delhi About the company: Launched in the year 1996 to offer the Indian Business community a platform to promote themselves globally tradeindia.com has created a niche as India's largest B2B marketplace, offering comprehensive business solutions to the Domestic and Global Business Community through its wide array of online services, directory services and facilitation of trade promotional events. Our portal is an ideal forum for buyers and sellers across the globe to interact and conduct business smoothly and effectively. With an unmatched expertize in data acquisition and online promotion, Tradeindia subsumes a huge number of company profiles and product catalogs under 2,256 different product categories and sub-categories. It is well promoted on all major search engines and receives an average of 20.5 million hits per month. Tradeindia is maintained and promoted by INFOCOM NETWORK LTD. Today we have reached a database of 47,66,674 registered users (Jan 2019), and the company is growing on a titanic scale with a considerable amount of new users joining/registering everyday, under the innovative vision and guidance of Mr. Bikky Khosla, CEO. Skills: Machine Learning (ML), Data engineering, Data Science, Big Data, Natural Language Processing (NLP) and Data Visualization

NVIDIAHQI 89

NVIDIA is in a unique position: we are developing AI-based products across multiple domains, and we collaborate with many interesting AI companies as partners and customers. Ensuring the highest Content Safety possible reduces exposure to inappropriate material. Preventing Bias and Discrimination is essential to both protect individual rights and achieve the best quality of results, including accuracy and completeness of information. By prioritizing safety and fairness, we can ensure that LLMs benefit everyone and contribute to a better future for all. Our team also works in the area of safety for generative models for language, robustness, and explainability. Our LLMs are a growing area of AI products, including models and services, and we are committed to ensuring that they are used safely and responsibly. We are looking for a talented Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness efforts for LLMs across all of our research and production engineering teams. In this role, you’ll have the opportunity to take on innovative problems in machine learning, particularly focused on safety for multi-modal LLMs. This role is directed at assessing, quantifying, and improving the safety and inclusivity of our LLM models in a scalable fashion. What you'll be doing: Develop the datasets and models for training and evaluating models and end-to-end systems for Content Safety, ProdSec, Robustness and ML Fairness. Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs. Define and track key metrics for responsible LLM behavior and usage. Follow the best MLOps practices of automation, monitoring, scale and safety. Contribute to the MLOps platform and develop safety tools to help ML teams be more effective. Collaborate with other engineers, data scientists, and researchers to develop and implement solutions to content safety and ML fairness challenges. What we need to see: Master’s or PhD in Computer Science, Electrical Engineering or related field - or equivalent experience. Minimum of 2+ years of work experience in developing and deploying machine learning models in production. Strong understanding of machine learning principles and algorithms. Hands-on programming experience in python and in-depth knowledge of machine learning frameworks, like Keras or PyTorch. Background in one or more of the following broader areas for 1+ years: Content Safety, ML Fairness, Robustness, AI Model Security, or related areas. Experience working in a range of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application. Practice working with large multi-modal datasets and multi-modal models. Good at problem-solving and analytical ability. Excellent collaboration and communication skills. Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty. Ways to stand out from the crowd: Skilled with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs (vision-language models) or any-to-text Proven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance. Knowledge of robustness, including hallucinations, digressions, and generative misinformation. Experience with GenAI security, including prompt stability, model extraction, confidentiality/data extraction, integrity, availability, and adversarial robustness. Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience. With highly competitive salaries and a comprehensive benefits package, Nvidia is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Large Language Models. If you're a creative engineer with a real passion for robust and enjoyable user experiences, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until October 10, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. NVIDIA pioneered accelerated computing. Today, our AI infrastructure powers global intelligence, transforming every industry. Learn more about NVIDIA.

Collective.workHQI 74

Contexte de la mission Marketplace e-commerce B2C présente dans plusieurs pays européens, le client réunit des milliers de vendeurs partenaires autour d'un catalogue de plusieurs millions de produits en constante évolution. L'équipe Machine Learning, orientée delivery et impact business, est aujourd'hui pleinement engagée dans la construction de produits IA et ML au cœur de la marketplace. L'enjeu est d'améliorer la qualité du catalogue pour rendre les produits plus faciles à trouver, à comparer et à utiliser. Le client recherche un Senior Machine Learning Engineer capable de concevoir, mettre en production et faire évoluer des solutions IA à grande échelle sur la catégorisation, l'extraction d'attributs, le matching et l'enrichissement de produits. Responsabilités * Concevoir et développer des solutions de ML et d'IA pour la qualité du catalogue : catégorisation, qualification, extraction d'attributs, matching et enrichissement de produits * Construire et maintenir des pipelines scalables de classification, de matching d'entités, de similarité sémantique et d'extraction d'attributs * Adapter et évaluer des modèles, dont des LLM et des modèles vision langage (VLM), sur des cas d'usage propres au catalogue * Concevoir des workflows combinant ML, modèles IA, règles métier et validation humaine pour fiabiliser la qualité des données produits * Définir et suivre les métriques de qualité catalogue et de performance des modèles, construire des benchmarks et restituer les résultats aux équipes tech et produit * Écrire du code prêt pour la production, déployer les systèmes IA et en garantir la fiabilité, l'observabilité et la performance * Travailler avec les développeurs, les Product Managers et les équipes métier pour cadrer les problèmes sous l'angle scientifique et business Profil recherché * Plus de 5 ans d'expérience en Machine Learning, Deep Learning ou AI Engineering, avec des modèles menés du prototype à la production à grande échelle * Expérience solide sur au moins une partie de ces sujets : catégorisation de produits, taxonomie, extraction d'attributs, entity resolution, matching de produits, similarité sémantique, embeddings, recherche d'information * Rigueur scientifique : conception de métriques, expérimentation, analyse d'erreurs et benchmarks pour orienter les décisions * Expérience de pipelines data et ML robustes en production (monitoring, fiabilité, performance) * Excellente maîtrise de Python et de SQL, avec un vrai souci de simplicité et de performance du code * Anglais professionnel courant, à l'écrit comme à l'oral * Fine tuning de LLM ou VLM, expérience e-commerce ou marketplace, pratiques MLOps et frameworks distribués (Spark, Ray, Dask) appréciés Stack technique Python, SQL, AWS, Airflow, Kubernetes, Snowflake, GitLab, bases vectorielles (PGVector), frameworks d'inférence et de fine tuning de LLM (OpenAI SDK, vLLM, Unsloth)

Cisco SystemsHQI 86

The application window is expected to close on: 10/11/2026 Meet the Team The Cisco CTO Organization is building the next generation of AI applications for networking and security. Our teams combine machine learning, generative AI, data engineering, cloud technologies, and domain expertise to build intelligent capabilities for Cisco’s networking and security products. Your Impact As a Software Engineer, you will work with experienced engineers and researchers to develop production-ready AI applications for networking and security use cases. You will contribute to AI-powered data pipelines, LLM applications, operational analytics, and automation that improve visibility, anomaly detection, incident analysis, troubleshooting, and customer experiences. What You’ll Do You will build scalable data pipelines and applied AI systems that transform complex networking and security telemetry into actionable operational intelligence. • Design ETL/ELT workflows in Python and SQL across cloud and distributed data platforms to process logs, alerts, graph structures, and semantic search datasets. • Build, evaluate, and monitor production LLM, RAG, and ML applications for automated troubleshooting, anomaly detection, and incident analysis. • Partner with senior engineers to write maintainable code, build user-facing insight dashboards, participate in design reviews, and support live production services. Minimum Qualifications • Meet one of the following (completed within the past 3 years or expected to be completed within the next 12 months): - A Master's degree with 0 years of work experience; or - A Bachelor's degree with 2 years of work experience; or - An educational or workforce development program (e.g., Associate’s, Apprenticeship, Boot camp, or Certification) with 3 years of work experience; or - A High school diploma (or equivalent) with 4 years of work experience. • Strong programming skills in Python and SQL • Knowledge of data structures, algorithms, software engineering, and object-oriented design Preferred Qualifications • Hands-on experience through internships, academic projects, research, or professional work in one or more of:Generative AI, LLMs, RAG, or machine learningData engineering and ETL/ELT pipelinesModel deployment or inferenceCloud-based application developmentWorkflow automation or analytics • Familiarity with Git, APIs, testing, and software development practices • Strong analytical, communication, and problem-solving skills • Experience with tools such as LangChain, Neo4j, AWS, Snowflake, Airflow, PySpark, TensorFlow, OpenCV, or React • Exposure to networking or security data, including metrics, logs, events, alerts, topology, or time-series data • Familiarity with network management, security operations, observability, or infrastructure automation • Experience building dashboards, chatbots, search systems, or AI-assisted workflows • Familiarity with LLM evaluation, prompt engineering, model optimization, or local inference • Relevant certifications, publications, research, or substantial technical projects Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Message to applicants applying to work in the U.S. and/or Canada: The starting salary range posted for this position is $165,000.00 to $211,100.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits. Individual pay is determined by the candidate's hiring location, market conditions, job-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process. U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time. U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies: • 10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees • 1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco • Non-exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full-time employees • Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations) • 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next • Additional paid time away may be requested to deal with critical or emergency issues for family members • Optional 10 paid days per full calendar year to volunteer For non-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies. Employees on sales plans earn performance-based incentive pay on top of their base salary, which is split between quota and non-quota components, subject to the applicable Cisco plan. For quota-based incentive pay, Cisco typically pays as follows: • .75% of incentive target for each 1% of revenue attainment up to 50% of quota; • 1.5% of incentive target for each 1% of attainment between 50% and 75%; • 1% of incentive target for each 1% of attainment between 75% and 100%; and • Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation. For non-quota-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid. The applicable full salary ranges for this position, by specific state, are listed below: New York City Metro Area: $165,000.00 - $242,800.00 Non-Metro New York state & Washington state: $148,700.00 - $216,100.00 * For quota-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined. ** Employees in Illinois, whether exempt or non-exempt, will participate in a unique time off program to meet local requirements.

ZendeskHQI 83

Job Description The Enterprise Machine Learning team drives organizational value through scalable ML solutions and data-driven insights, fundamentally changing how business decisions are made. We collaborate closely with stakeholders, applying the latest advances in machine learning and statistical modeling to create highly impactful outcomes. Our commitment is to advance the state of applied science and robust system design to enhance and expand our core business capabilities. Role Overview We've spent the past year consolidating and validating our revenue data, most signals now live in one place. The next step is building a customer intelligence layer: systems that dynamically learn which signals drive outcomes, adapt as the business evolves, and surface insights that change how we act. As an Applied Machine Learning Scientist, you will be the person who makes that happen. You will train models, design experiments, and uncover the patterns that connect customer behavior to revenue outcomes.Then ship those insights as production systems that the business relies on daily. You own problems end-to-end: from formulating the right question, to training and validating models, to deploying them and measuring whether they actually moved the needle, in a closed feedback loop. You will work alongside a team of AI/ML Engineers, Data Engineers, and Analysts as part of the Enterprise Data & Analytics department. Your focus is the science: understanding what drives outcomes, building models that learn from data, and turning that understanding into systems that work. Key Responsibilities: • Design, train, evaluate, and deploy machine learning models that predict and explain revenue-related outcomes, including churn, expansion, conversion, and customer engagement. • Build and own end-to-end ML systems, from data preparation and experimentation through production deployment, continuous retraining, monitoring, and performance optimization. • Develop intelligence from structured and unstructured data, applying statistical modeling, modern NLP, embeddings, LLMs, and deep learning where they create measurable business value. • Design and run experiments to uncover the relationships between customer behavior, product usage, and business outcomes, translating model outputs into actionable insights. • Partner closely with Product, Engineering, and business stakeholders to identify high-impact opportunities, prioritize ML initiatives, and embed intelligence into decision-making workflows. • Measure and maximize business impact by monitoring model performance, detecting drift, validating outcomes, and continuously iterating based on real-world results and feedback. This Role Is For You If… • You have 3–5 years of experience in Applied Machine Learning, Data Science, or a related quantitative field, with a track record of building models that deliver measurable business impact. • You bring foundation in machine learning and statistics, with hands-on experience training models on messy, real-world data. You're comfortable dealing with label noise, class imbalance, feature leakage, data drift, and designing robust validation strategies. • You are experienced in applying ML to both structured and unstructured data, including modern NLP techniques, embeddings, LLMs, or deep learning where they create meaningful business value. • You have production-grade engineering skills, including Python, SQL, software engineering best practices, and experience deploying, monitoring, and maintaining ML models. • You have pragmatic, product-oriented mindset. You get more satisfaction from shipping a simple model that improves a business metric than building a complex one with marginally better offline performance. You think in terms of business outcomes, user behavior, and measurable impact—not model sophistication alone. • You proactively identify high-impact opportunities, form opinions based on data and user understanding, and continuously iterate on your work. #LI-MK12 The intelligent heart of customer experience Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love. Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week. As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law. Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here . Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.

RobloxHQI 93

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Principal Platform Engineer who treats platform as a product : someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs and easy-to-use CLIs/UIs that our internal developers love. This role blends product thinking, developer experience, backend engineering, and infrastructure at scale. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must. You Are Have 7+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox. A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands. Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use. Good to have - experience with the end‑to‑end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use. You're passionate about infrastructure‑as‑code and automating painful manual processes. You push for platform solutions that don’t leave on-call teams carrying the burden of design choices. Passionate about supporting ML engineers to meet and understand their needs, and translating them into clean, durable platform abstractions. A clear communicator who excels at both written and verbal communication across differing levels of technical detail. Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field. You Will Own platform as a product and set direction end to end: Define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs that make ML@Roblox easy to adopt. Bootstrap and maintain core ML Platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes. Set technical strategy and oversee development of high scale and reliable infrastructure systems, with clear SLOs for latency, availability, and cost. Design great developer experiences with paved‑road templates, golden paths, opinionated defaults, and clear docs to reduce time‑to‑first‑production. Instrument the platform to measure adoption, friction, reliability, and cost; use data to prioritize roadmap and validate outcomes. Partner across organizations (ML Engineering, Data Science, Infra/SRE, Security, Finance) to optimize performance, safety, and spend, especially for GPU‑intensive training and high‑QPS inference. Propose and implement new platform tooling to improve time to production for MLEs across the full ML lifecycle. Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading‑edge technologies and practices. Mentor junior and senior engineers, lead design reviews, and drive cross‑team architectural decisions that last. For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page . Annual Salary Range $243,290 — $312,970 USD Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted). Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process. For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.

PinterestHQI 91

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here . Pinterest is a visual discovery platform where hundreds of millions of people come to find inspiration and decide what to try, buy, or do next. Our Ads Quality organization builds the machine-learning systems that make ads relevant and valuable to Pinners while delivering meaningful outcomes for advertisers. We are seeking a Director of Machine Learning Engineering to lead a broad portfolio of Ads Quality modeling teams focused on engagement, conversion, ROAS optimization, ranking, representation learning, and ML-powered experimentation. In this role, you will shape and drive a unified technical strategy across Ads Quality, leading teams responsible for engagement ranking, oCPM and conversion modeling, ROAS optimization, lightweight ranking and retrieval models, foundation model adoption, sequence and multimodal modeling, and the quality and efficiency of production machine learning systems. What you’ll do: Set the technical vision and multi-year strategy for Ads Quality machine learning, connecting model innovation to Pinner value, advertiser performance, revenue, and marketplace health. Lead and develop a group of engineering managers, senior technical leaders, and machine-learning engineers across multiple modeling domains. Establish a coherent modeling roadmap across engagement, conversion, ROAS, relevance, ranking, and foundation-model initiatives. Drive improvements in model quality, calibration, generalization, cold-start performance, attribution, and robustness across Pinterest surfaces. Guide the evolution of Ads models toward larger, more generalizable architectures, including foundation models, distillation, long-context sequence modeling, multimodal representations, and cross-domain learning. Ensure that modeling investments translate into reliable production outcomes through strong offline evaluation, online experimentation, launch discipline, and post-launch monitoring. Partner closely with Ads Product, Ads Data Science, Ads Signals, Ads Retrieval, Ads Delivery, Measurement, Core, ATG, and ML Infrastructure. Set expectations for training-serving parity, data quality, privacy, reliability, latency, capacity, and cost efficiency. Improve engineering velocity through better experimentation workflows, reusable modeling infrastructure, automation, and agentic development tools. Build a culture of technical excellence, candid collaboration, inclusion, ownership, and continuous learning. Represent Ads Quality ML in senior leadership forums and communicate strategy, tradeoffs, risks, and results clearly to technical and non-technical audiences. What we’re looking for: Minimum 12 years of experience building and deploying machine-learning systems, including significant experience leading managers and multi-team organizations. Demonstrated success leading large-scale recommendation, ranking, advertising, search, marketplace, or personalization ML teams. Strong understanding of modern deep-learning and recommender-system techniques, including sequence models, embeddings, multimodal models, multi-task learning, foundation models, distillation, and reinforcement learning. Experience with conversion, value, ROAS, bidding, or other lower-funnel optimization problems is strongly preferred. Proven ability to connect modeling objectives and offline metrics to online experiments and business outcomes. Experience operating production ML systems with demanding requirements for latency, availability, calibration, privacy, reliability, and cost. Strong judgment in balancing near-term product delivery with foundational technical investments. Track record of building high-performing organizations, developing senior leaders, and creating effective operating mechanisms. Excellent communication and collaboration skills, with the ability to influence across organizational boundaries. Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience; advanced degree preferred. Relocation Statement : This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model. In-Office Requirement Statement : We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role. This role will need to be in the office for in-person collaboration 1-2x per week and therefore needs to be in a commutable distance from one of the following offices: Palo Alto, San Francisco. #LI-SM4 #LI-HYBRID At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here . US based applicants only $314,580 — $550,515 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support. By submitting this application, I certify that all information submitted in my application and throughout the hiring process is true, accurate, and complete to the best of my knowledge. I understand that any false statement, omission, or misrepresentation may disqualify me from employment consideration or result in termination if discovered after hire.

LyftHQI 89

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with petabyte-scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. As a PhD Machine Learning Engineer Intern on our Applied AI team, you'll take on an open research problem tied to product experiences used by millions of riders. Working closely with a Staff ML Engineer mentor, you'll scope the problem, develop and evaluate new methods on real data, and take the work far enough that it can be shared with the research community, with the goal of a paper submission to a top ML venue. If you are a PhD student who enjoys turning open-ended research questions into working systems, and you want your research to be tested against real users and real data, this opportunity is for you! Responsibilities: Own a research project from start to finish: frame the problem, review related work, propose new methods, and design rigorous offline and online evaluations Design, build, train and test ML models in areas such as reinforcement learning, sequential decision-making, personalization Write production-quality code that turns research prototypes into working pipelines on Lyft's data and ML infrastructure Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame research questions within the business context Analyze experimental and observational data, and communicate findings clearly to both technical and non-technical audiences Write up results for publication at a peer-reviewed venue, with support from your mentor and the team Participate in code and spec reviews to ensure code quality and distribute knowledge Experience: Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Operations Research, Applied Mathematics, or a related technical field, and returning to your program after the internship, with a graduation date between December 2027 and Summer 2028 (required) A track record of ML research, shown through publications, preprints, or substantial research projects Strong foundation in reinforcement learning and sequential decision making, especially problems with delayed or long-horizon rewards Solid grounding in both causal inference and counterfactual evaluation Good understanding of ML libraries like PyTorch, TensorFlow, or JAX Strong programming skills in Python or a similar language Proven ability to effectively turn research ML papers into working code Curiosity and ability to quickly learn new concepts and technologies Strong problem solving mindset, resourcefulness, and willingness to figure things out independently through research or collaboratively through brainstorming Demonstrated oral and written communication skills Bonus Points Publications at venues such as NeurIPS, ICML, ICLR, KDD, WWW, RecSys, or AAAI Experience with offline reinforcement learning, off-policy evaluation, or learning from logged interaction data, recommender systems or personalization Practical knowledge of how to build efficient end-to-end ML workflows on large-scale data (for example Spark or SQL) Familiarity with online experimentation and A/B testing Benefits: Great medical, dental, and vision insurance options Mental health benefits In addition to holidays, interns receive 2 days paid time off and 3 days sick time off 401(k) plan to help save for your future Subsidized commuter benefits Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law. Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. The expected base pay range for this position in the San Francisco area is $65-$68/hour. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.