Sr Machine Learning Engineer

Uber is hiring a Senior Machine Learning Engineer (onsite) in San Francisco to build and own machine learning models for membership offer relevance and messaging personalization across Uber and Uber Eats.

Responsibilities

• Own end-to-end lifecycle for targeting and personalization models, including problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.
• Develop heterogeneous treatment effect models to estimate incremental impact of interventions on users.
• Create budget-constrained allocation systems that convert per-user uplift predictions into offer decisions under real constraints, including incentive budget, variable contribution targets, cannibalization of full-price conversion, and per-surface frequency caps.
• Build personalized ranking and sequencing models for membership messaging across Eats and Mobility apps, balancing conversion, user experience, and contention with non-member content.
• Collaborate with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, ensuring production behavior matches offline results.
• Partner with Product, Engineering, Data Science, Finance, and Marketing to translate ambiguous business goals into concrete ML problem statements.

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 drive product or business decisions in production.
• Proficiency in Python and modern ML frameworks, including PyTorch, scikit-learn, and XGBoost/LightGBM or equivalents.
• Strong SQL skills and hands-on experience with large-scale data processing using Spark, Hive, Presto, or comparable tools.
• Experience designing and analyzing experiments, including A/B testing, power analysis, variance reduction, and responsible interpretation of noisy results.
• Experience delivering models across the full lifecycle, from notebooks to production pipelines, serving, monitoring, retraining, and deployment.
• Ability to explain modeling decisions and associated business consequences clearly to both technical and non-technical audiences.

Technologies

• 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 business metrics and modeling, including trial-to-paid conversion, retention curves, LTV modeling, cannibalization, and incrementality measurement.
• Experience leading technical direction across an ambiguous, cross-functional scope.

Salary

• USD 202,000 - 224,000 per year
• For San Francisco, CA-based roles: base salary range is USD $202,000 per year - USD $224,000 per year

Benefits

• Eligible to participate in Uber’s bonus program; may be offered an equity award and other types of compensation.
• All full-time employees are eligible to participate in a 401(k) plan.
• Eligibility for various benefits.

Work Location

• San Francisco, CA (onsite)
• Unless approved for full remote work, employees must spend at least 50% of their time in-office.
• Some roles (including those at greenlight hubs) require full-time in-office presence.

Equal Opportunity

• Uber considers qualified applicants 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.
• Uber also considers applicants regardless of criminal histories, consistent with legal requirements.
• Accommodation: if you have a disability or special need that requires accommodation, complete the accommodation form.