Senior ML Engineer — Personalization & Offer Optimization

Uber Technologies Inc.; 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 offer relevance and messaging personalization for Uber One across rides and Eats. You will own end-to-end modeling—from problem framing to deployment—balancing business impact with user experience across multiple surfaces.

You will build models for which users see which offers and messages, with budget-aware allocation and personalized ranking, collaborating with cross-functional teams to translate business goals into ML objectives.

Qualifications

• 5+ years of experience shipping ML models in production.
• Proficiency in Python and ML frameworks (PyTorch, scikit-learn, XGBoost/LightGBM).
• Strong SQL and large-scale data processing (Spark, Hive, Presto).
• Experience with experimental design (A/B testing, power analysis).
• Ability to take models from notebook to production and back.
• Ability to explain modeling decisions to technical and non-technical audiences.

Responsibilities

• Own end-to-end targeting and personalization models lifecycle.
• Build heterogeneous treatment effect models for interventions.
• Design budget-constrained allocation systems for offers.
• Develop personalized ranking for membership messaging across Uber apps.
• Partner with engineers to productionize models in real-time and batch paths.
• Collaborate with product, data science, finance, and marketing to frame ML problems.

Skills

Python

Experiment design

Communication

Production ML systems

Big data tools

Education

Bachelor's degree in Computer Science or related quantitative field

Tools

PyTorch

scikit-learn

XGBoost/LightGBM

Spark

Hive/Presto

Job description

Uber is seeking a Senior Machine Learning Engineer to advance offer relevance and messaging personalization for Uber One across rides and Eats. You will own end-to-end modeling—from problem framing to deployment—balancing business impact with user experience across multiple surfaces.

You will build models for which users see which offers and messages, with budget-aware allocation and personalized ranking, collaborating with cross-functional teams to translate business goals into ML objectives.