Uber is hiring a Senior Machine Learning Engineer in San Francisco, CA (onsite) to develop and own models that improve membership offer relevance and message personalization across Uber and Uber Eats. This role combines end-to-end ML lifecycle ownership with real-world decisioning, including incentive targeting, budget-aware allocation, and personalized ranking and sequencing across multiple surfaces.
What you’ll work on
- Own the full lifecycle of targeting and personalization models, including problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.
- Build heterogeneous treatment effect models to estimate incremental impact of interventions on users.
- Design budget-constrained allocation systems that convert per-user uplift predictions into offer decisions under constraints such as incentive budget, variable contribution targets, cannibalization of full-price conversion, and per-surface frequency caps.
- Create personalized ranking and sequencing models for membership messaging across Eats and Mobility apps, balancing conversion, user experience, and contention with non-member content.
- Partner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, ensuring production behavior matches offline results.
- Collaborate with Product, Engineering, Data Science, Finance, and Marketing to translate evolving business goals into concrete ML problem definitions.
Key requirements
- 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 influence product or business decisions in production.
- Strong proficiency in Python and modern ML frameworks such as PyTorch and scikit-learn, plus XGBoost/LightGBM or equivalent.
- Strong SQL skills and hands-on experience with large-scale data processing (e.g., Spark, Hive, Presto, or comparable).
- Proven experience in experimental design and analysis, including A/B testing, power analysis, variance reduction, and responsible interpretation of noisy results.
- Experience across the model lifecycle from notebook to production pipelines, serving, monitoring, retraining, and deployment.
- Ability to clearly explain modeling decisions and business consequences to both technical and non-technical stakeholders.
Tools you’ll use
Python, PyTorch, scikit-learn, XGBoost, LightGBM, SQL, Spark, Hive, Presto
Preferred qualifications
- Experience training deep feed-forward models (MLP) for uplift estimation.
- Experience with constrained optimization for resource allocation, including 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, including trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.
- Experience leading technical direction across an ambiguous, cross-functional scope.
Compensation and benefits
Salary range: USD 202,000 - 224,000 per year (San Francisco, CA-based roles).
- You may be eligible to participate in Uber’s bonus program, and may be offered an equity award and other types of compensation.
- All full-time employees are eligible to participate in a 401(k) plan.
- You will also be eligible for various benefits.
Work location
- 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, such as those at greenlight hubs, require full-time in-office presence.
Equal opportunity
Uber is proud to be an equal opportunity employer. All qualified applicants will receive consideration 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. Uber also considers qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please provide details by completing Uber’s accommodation form.
