{"schemaVersion":"jobsearcher.job.v1","id":"4a06e99f4c43ad22d8ec9daa","url":"https://jobsearcher.com/jobs/4a06e99f4c43ad22d8ec9daa","canonicalUrl":"https://jobsearcher.com/jobs/4a06e99f4c43ad22d8ec9daa","title":"Sr Machine Learning Engineer","description":"About the role and team\n\nUber has evolved from a simple ride-hailing app into a global \"go-get\" powerhouse. At the heart of this evolution is Uber One, our premier membership program that bridges the gap between Rides, Eats, and beyond. With over 45 million members and counting, Uber One is our most powerful growth engine.\n\nMembership growth depends on getting the right offer in front of the right member at the right moment, across a broad set of products, surfaces, and touchpoints. As the program has scaled, so has the complexity of those decisions - and the opportunity to make them more relevant, more efficient, and more measurable.\n\nWe are seeking a Senior Machine Learning Engineer to help advance how Membership approaches offer relevance and messaging personalization. In this role, you will develop and own models that inform which users are shown which offers and communications, on which surfaces, and at what time - spanning incentive targeting, budget-aware allocation, and personalized ranking of messaging across the Uber and Uber Eats apps.\n\nWhat the Candidate Will Do\nOwn the end-to-end lifecycle of targeting and personalization models - problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.\nBuild heterogeneous treatment effect models that predict the incremental impact of interventions on users.\nDesign budget-constrained allocation systems that turn per-user uplift predictions into offer decisions under real constraints (incentive budget, variable contribution targets, cannibalization of full-price conversion, per-surface frequency caps).\nBuild personalized ranking and sequencing models for membership messaging across Eats and Mobility apps - balancing conversion against user experience and contention with non-membership content.\nPartner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, and make sure they behave in production the way they did offline.\nWork across Product, Engineering, Data Science, Finance, and Marketing to translate fuzzy business goals into concrete ML problem statements.\nBasic Qualifications\nBachelor's degree in Computer Science, Statistics, Economics, Operations Research, or a related quantitative field, or equivalent practical experience.\n5+ years of experience building and shipping ML models that drive product or business decisions in production.\nStrong proficiency in Python and modern ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM or equivalent).\nStrong SQL and hands-on experience with large-scale data processing (Spark, Hive, Presto, or comparable).\nDemonstrated experience with experimental design and analysis - A/B testing, power analysis, variance reduction, and interpreting noisy results responsibly.\nExperience building a model across all lifecycle stages: from notebook to production pipelines, serving, monitoring, retraining, and deployment.\nAbility to explain a modeling decision and its business consequences clearly to technical and non-technical audiences alike\nPreferred Qualifications\nExperience training deep feed-forward models (MLP) for uplift estimation.\nExperience with constrained optimization applied to resource allocation (LP/MIP, Lagrangian duality, dual-price or bidding-style budget pacing).\nExperience with incentive, promotion, pricing, or discount targeting at consumer scale.\nExperience with contextual bandits or reinforcement learning for sequential decisioning.\nFamiliarity with subscription businesses: trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.\nExperience leading technical direction across an ambiguous, cross-functional scope.\n\nFor San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.\n\nYou will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.\n\nReady to Ride?\n\nThis isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.\n\nYou may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.\n\nOffices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.\n\nUber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.","company":"Uber","rawCompany":"uber","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-09-18T09:16:15.590Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"485310","title":"Taxi and Ridesharing Services","slug":"taxi-and-ridesharing-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Sr Machine Learning Engineer","description":"About the role and team\n\nUber has evolved from a simple ride-hailing app into a global \"go-get\" powerhouse. At the heart of this evolution is Uber One, our premier membership program that bridges the gap between Rides, Eats, and beyond. With over 45 million members and counting, Uber One is our most powerful growth engine.\n\nMembership growth depends on getting the right offer in front of the right member at the right moment, across a broad set of products, surfaces, and touchpoints. As the program has scaled, so has the complexity of those decisions - and the opportunity to make them more relevant, more efficient, and more measurable.\n\nWe are seeking a Senior Machine Learning Engineer to help advance how Membership approaches offer relevance and messaging personalization. In this role, you will develop and own models that inform which users are shown which offers and communications, on which surfaces, and at what time - spanning incentive targeting, budget-aware allocation, and personalized ranking of messaging across the Uber and Uber Eats apps.\n\nWhat the Candidate Will Do\nOwn the end-to-end lifecycle of targeting and personalization models - problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.\nBuild heterogeneous treatment effect models that predict the incremental impact of interventions on users.\nDesign budget-constrained allocation systems that turn per-user uplift predictions into offer decisions under real constraints (incentive budget, variable contribution targets, cannibalization of full-price conversion, per-surface frequency caps).\nBuild personalized ranking and sequencing models for membership messaging across Eats and Mobility apps - balancing conversion against user experience and contention with non-membership content.\nPartner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, and make sure they behave in production the way they did offline.\nWork across Product, Engineering, Data Science, Finance, and Marketing to translate fuzzy business goals into concrete ML problem statements.\nBasic Qualifications\nBachelor's degree in Computer Science, Statistics, Economics, Operations Research, or a related quantitative field, or equivalent practical experience.\n5+ years of experience building and shipping ML models that drive product or business decisions in production.\nStrong proficiency in Python and modern ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM or equivalent).\nStrong SQL and hands-on experience with large-scale data processing (Spark, Hive, Presto, or comparable).\nDemonstrated experience with experimental design and analysis - A/B testing, power analysis, variance reduction, and interpreting noisy results responsibly.\nExperience building a model across all lifecycle stages: from notebook to production pipelines, serving, monitoring, retraining, and deployment.\nAbility to explain a modeling decision and its business consequences clearly to technical and non-technical audiences alike\nPreferred Qualifications\nExperience training deep feed-forward models (MLP) for uplift estimation.\nExperience with constrained optimization applied to resource allocation (LP/MIP, Lagrangian duality, dual-price or bidding-style budget pacing).\nExperience with incentive, promotion, pricing, or discount targeting at consumer scale.\nExperience with contextual bandits or reinforcement learning for sequential decisioning.\nFamiliarity with subscription businesses: trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.\nExperience leading technical direction across an ambiguous, cross-functional scope.\n\nFor San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.\n\nYou will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.\n\nReady to Ride?\n\nThis isn't the kind of place where you follow a playbook - it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves - we'd love to hear from you.\n\nYou may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.\n\nOffices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.\n\nUber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.","datePosted":"2026-09-18T09:16:15.590Z","dateModified":"2026-09-18T09:16:15.590Z","hiringOrganization":{"@type":"Organization","name":"Uber","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"4a06e99f4c43ad22d8ec9daa"},"url":"https://jobsearcher.com/jobs/4a06e99f4c43ad22d8ec9daa"}}