Drimva

Uber

Sr Staff Machine Learning Engineer- Consumer Incentives

$267K–$297K

Sunnyvale, California +3 · Senior Staff · Full-time · Posted Oct 23, 2026

Location
Sunnyvale, California
San Francisco, California
New York City, New York
Seattle, Washington
Experience
12+ years of ML experience.
Education
Master’s degree or Ph.D. in Computer Science, Engineering, or Mathematics.

About the job

Sets technical strategy and leads development of scalable machine-learning systems that personalize consumer incentives, measure their incremental impact, and optimize investment.

What you'll do

  • Define the long-term ML vision, architecture, and roadmap; lead strategic initiatives across teams.
  • Design and guide scalable ML systems and models for personalization, impact measurement, and constrained optimization.
  • Lead rigorous evaluation and experimentation, explore emerging AI approaches, and mentor engineers and technical leaders.

What they ask for

  • Master’s degree or Ph.D. in Computer Science, Engineering, or Mathematics.
  • 12+ years of ML experience, including significant work on large-scale deep learning systems.
  • Demonstrated ownership of high-impact ML systems such as search, recommendations, personalization, or targeting.
  • Proven ability to set technical strategy for a large organization and influence executive-level product roadmaps; strong product intuition connecting model improvements to business outcomes.

Nice to have

  • Track record launching multi-year, multi-organization ML initiatives that drove step-change business outcomes.
  • Experience championing multi-year technical roadmaps across large engineering organizations and raising standards through mentorship.

Pay

USD $267,000 per year - USD $297,000 per year (base salary)

Tools

deep learning, constrained optimization, reinforcement learning, foundation models, generative AI

AI focus

Develops AI/ML for consumer incentive personalization, impact estimation, and optimization; also evaluates and advances reinforcement learning, foundation models, and generative AI for products.

Model-written summary · the original posting is authoritative

First seen Oct 24, 2026 · Last verified Nov 5, 2026 (UTC)