Sr Machine Learning Engineer

Uber; San Francisco (CA); On-site; USD 202,000 - 224,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Benefits

Job summary

Uber is seeking a Senior Machine Learning Engineer to advance how membership offers are personalized across Uber and Uber Eats. You will own models that determine which offers and messages to show, on which surfaces, and at what time, balancing incentives, budget constraints, and user experience.

You will partner with backend and platform engineers to productionize models in real-time paths and batch pipelines, translating fuzzy business goals into concrete ML problems that drive measurable

Qualifications

• Bachelor's degree in a quantitative field or equivalent practical experience.
• 5+ years of experience shipping ML models in production.
• Proficiency in Python and modern ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM).
• Strong SQL and large-scale data processing experience (Spark, Hive, Presto).
• Experience with experimental design and A/B testing.
• Experience with end-to-end ML lifecycle: notebook to production, serving, monitoring, retraining.

Responsibilities

• Own end-to-end targeting and personalization models lifecycle, including framing, data, training, offline evaluation, online experiments, deployment, and monitoring.
• Build heterogeneous treatment effect models predicting incremental impact of interventions.
• Design budget-constrained allocation systems for offer decisions under constraints.
• Develop personalized ranking and sequencing models for membership messaging across Uber apps.
• Collaborate with backend/platform engineers to productionize models in real-time serving paths and batch pipelines.
• Translate business goals into concrete ML problem statements across product, engineering, data science, finance, and marketing.

Skills

Python

SQL

ML lifecycle

A/B testing

Communication

Education

Bachelor's degree in CS or related field

Tools

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.

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.