Sr Machine Learning Engineer

Uber; United States; Hybrid; USD 202,000 - 224,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Various benefits

Job summary

Uber is seeking a Senior Machine Learning Engineer to advance offer relevance and messaging personalization for Uber One. You will own end-to-end modeling, from problem framing to deployment, across Rides, Eats, and related surfaces, delivering personalized offers and messaging at scale.

You will build and evaluate uplift models, design budget-aware allocation, and collaborate with cross-functional teams to translate business goals into concrete ML problems and measurable outcomes.

Qualifications

• Bachelor's degree in Computer Science, Statistics, Economics, Operations Research, or 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

Responsibilities

• 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.
• Build personalized ranking and sequencing models for membership messaging across Eats and Mobility apps.
• Partner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines.
• Work across Product, Engineering, Data Science, Finance, and Marketing to translate fuzzy business goals into concrete ML problem statements.

Skills

ML model development

Cross-functional collaboration

Experiment design

Production ML deployment

Education

Bachelor's degree in CS/Statistics/OR related

Tools

Python

PyTorch

scikit-learn

XGBoost/LightGBM

Spark

Hive/Presto

Job description

About the role and team

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.

What the Candidate Will Do

• 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.

Basic Qualifications

• 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.

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.

Offices 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.

Uber 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.

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.