Sr Machine Learning Engineer

• Own the end-to-end lifecycle of targeting and personalization models - problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.
• Build heterogeneous treatment effect models that predict the incremental impact of interventions on users.
• Design 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).
• Build personalized ranking and sequencing models for membership messaging across Eats and Mobility apps - balancing conversion against user experience and contention with non-membership content.
• Partner 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.
• Work across Product, Engineering, Data Science, Finance, and Marketing to translate fuzzy business goals into concrete ML problem statements.

• Bachelor’s degree in Computer Science, Statistics, Economics, Operations Research, or a related quantitative field, or equivalent practical experience.
• 5+ years of experience building and shipping ML models that drive product or business decisions in production.
• Strong proficiency in Python and modern ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM or equivalent).
• Strong SQL and hands-on experience with large-scale data processing (Spark, Hive, Presto, or comparable).
• Demonstrated experience with experimental design and analysis - A/B testing, power analysis, variance reduction, and interpreting noisy results responsibly.
• Experience building a model across all lifecycle stages: from notebook to production pipelines, serving, monitoring, retraining, and deployment.
• Ability to explain a modeling decision and its business consequences clearly to technical and non-technical audiences alike

Preferred Qualifications

• Experience training deep feed-forward models (MLP) for uplift estimation.
• Experience with constrained optimization applied to resource allocation (LP/MIP, Lagrangian duality, dual-price or bidding-style budget pacing).
• Experience with incentive, promotion, pricing, or discount targeting at consumer scale.
• Experience with contextual bandits or reinforcement learning for sequential decisioning.
• Familiarity with subscription businesses: trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.
• Experience leading technical direction across an ambiguous, cross-functional scope.

For San Francisco, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

You 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.

Ready to Ride?

This 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.

You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits. (https://jobs.uber.com/en/benefits/)

Uber 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.

Membership 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.