About the RoleUber One is Uber's flagship membership program, serving more than 50 million members globally across Mobility, Delivery, Grocery, and other Uber experiences. Membership combines savings, benefits, priority experiences, and exclusive offerings while driving engagement, cross-platform adoption, and long-term retention.
As a Senior Machine Learning Engineer, you will build product-focused ML systems for targeting, personalization, offers, and member engagement. Your models will directly influence the Membership experiences consumers see across Uber and Uber Eats.
What You Will Do- Own targeting and personalization models from problem framing through deployment and monitoring.
- Build treatment-effect and uplift models to estimate incremental impact of Membership interventions.
- Design budget-aware allocation systems for consumer offers under real business constraints.
- Build ranking and sequencing models for Membership messaging across Mobility and Delivery.
- Productionize models in real-time serving paths and large-scale batch pipelines.
- Design and analyze experiments to measure incremental product impact.
- Translate ambiguous Membership growth problems into concrete ML solutions with cross-functional partners.
Basic Qualifications- 5+ years of experience building and shipping production ML models.
- Strong proficiency in Python and modern ML frameworks such as PyTorch, scikit-learn, XGBoost, or LightGBM.
- Strong SQL and large-scale data processing experience with Spark, Hive, Presto, or similar technologies.
- Experience with experimental design, A/B testing, and measurement of noisy product outcomes.
- Experience owning ML systems from offline development through serving, monitoring, and retraining.
- Experience applying ML to user-facing product or business decisions.
- Bachelor's degree in a quantitative field or equivalent practical experience.
Preferred Qualifications- Experience applying ML to membership, subscription, growth, or consumer engagement products.
- Experience with uplift modeling, treatment effects, ranking, recommendation, or sequential decision systems.
- Experience with constrained optimization, incentives, promotions, pricing, or offer targeting at consumer scale.
- Strong product mindset with experience translating consumer needs and business constraints into measurable ML outcomes.
- Experience collaborating across Product, Engineering, Data Science, Finance, or Marketing on complex product decisions.
- Familiarity with subscription metrics including conversion, retention, LTV, cannibalization, and incrementality.
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.
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. Ask your Recruiter for details about this role's requirements.
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. If you have a disability or special need that requires accommodation, please let us know by completing this form.
