Sr Machine Learning Engineer

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.

We 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.

• 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

• 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.