Sr Machine Learning Engineer

Uber is seeking a Senior Machine Learning Engineer to develop and take ownership of models that drive membership offer relevance and messaging personalization across Uber and Uber Eats, including incentive targeting, budget-aware allocation, and personalized ranking and sequencing. This role spans the full model lifecycle, from offline evaluation through deployment, monitoring, and experimentation.

Role Focus

• Own end-to-end lifecycle of targeting and personalization models, including problem framing, data, training, offline evaluation, online experimentation, deployment, and monitoring.
• Develop heterogeneous treatment effect models to estimate incremental user impact of interventions.
• Create budget-constrained allocation systems that convert per-user uplift predictions into offer decisions under real constraints such as incentive budgets, 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 with user experience and contention against non-membership content.

Responsibilities

• Partner with backend and platform engineers to productionize models in real-time serving paths and batch pipelines, ensuring production behavior matches offline performance.
• Collaborate across Product, Engineering, Data Science, Finance, and Marketing to translate business objectives into concrete ML problem statements.

Required 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 including PyTorch and scikit-learn, plus XGBoost/LightGBM or equivalent.
• Strong SQL skills and hands-on experience with large-scale data processing using Spark, Hive, Presto, or comparable tools.
• Demonstrated experience with experimental design and analysis, including A/B testing, power analysis, variance reduction, and responsible interpretation of noisy results.
• Experience covering all model lifecycle stages, from notebook development through production pipelines, serving, monitoring, retraining, and deployment.
• Ability to explain modeling decisions and their business consequences clearly to technical and non-technical audiences.

Preferred Qualifications

• Experience training deep feed-forward models (MLP) for uplift estimation.
• Experience with constrained optimization for resource allocation, such as LP/MIP, Lagrangian duality, dual-price approaches, 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.

Technology Stack

• Python, PyTorch, scikit-learn
• XGBoost, LightGBM
• SQL
• Spark, Hive, Presto

Location and Compensation

Location: San Francisco, CA (onsite)

Salary: USD 202,000 - 224,000 per year

Base salary range (San Francisco, CA-based roles): 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 Model

• 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 Employer

• Uber provides 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.
• Uber also considers applicants regardless of criminal histories, consistent with legal requirements.
• Applicants with disabilities or special needs that require accommodation should complete the accommodation form.