Senior Staff Engineer, Matching & Segmentation

Uber Uber · Consumer · San Francisco, CA · Engineering

Senior Staff Engineer, Tech Lead for Uber's Matching & Segmentation organization, focusing on ML-powered systems for real-time rider-driver matching and marketplace segmentation. The role involves architecting, developing, and deploying ML and optimization systems at scale, leading cross-org initiatives, and mentoring engineers. Experience with backend development, distributed systems, ML systems, and optimization algorithms is required.

What you'd actually do

  1. Be the Tech Lead for a complex domain within Matching & Segmentation, setting technical direction and driving architecture decisions across matching algorithms, segmentation models, forecasting systems, and real-time marketplace infrastructure.
  2. Design, develop, and deploy ML and optimization systems that solve high-impact business problems at scale — including real-time matching, reinforcement learning-based dispatch, and experiment-driven product development.
  3. Lead projects that span across orgs (e.g., matching, driver pricing, rider pricing, surge, platform) with significant cross-org dependencies and design complexity.
  4. Collaborate closely with Scientists, Product Managers, and peer engineering teams to define technical strategy, translate business requirements into system designs, and deliver high-quality solutions.
  5. Drive ongoing improvements in system reliability, performance, scalability, and efficiency through strong engineering practices, automation, and observability.

Skills

Required

  • backend development
  • distributed systems
  • system design for large-scale, low-latency applications
  • ML systems
  • optimization algorithms
  • real-time decision systems
  • leading complex, multi-team technical initiatives
  • mentoring engineers

Nice to have

  • marketplace systems
  • matching/ranking algorithms
  • reinforcement learning
  • forecasting systems
  • large-scale online experiments
  • Go
  • Java
  • Python
  • performance optimization
  • debugging
  • analytical and problem-solving skills

What the JD emphasized

  • ML systems
  • optimization algorithms
  • real-time decision systems
  • reinforcement learning
  • marketplace systems
  • matching/ranking algorithms

Other signals

  • ML-powered systems
  • real-time matching
  • marketplace segmentation
  • optimization and systems problems
  • reinforcement learning-based dispatch