Lead Product Manager, Av Labs

Uber Uber · Consumer · San Francisco, CA +1 · Product

Lead Product Manager for Uber's AV Labs, focusing on building platforms to unlock real-world, long-tail driving data for autonomous vehicles. The role involves owning product strategy, roadmap, and execution for core AV Labs domains including machine learning and data platforms, translating technical goals into product requirements, and partnering closely with engineering and science teams.

What you'd actually do

  1. Own product strategy, roadmap, and execution for a core AV Labs domain spanning one or more of the following domains: hardware systems, machine learning, semantic understanding, and data & validation platforms.
  2. Translate broad business and technical goals into clear product requirements, launch plans, milestones, and success metrics.
  3. Develop comprehensive PRDs and data-driven trade-off matrices to evaluate competing solutions based on scalability, cost, and technical constraints.
  4. Partner closely with engineering and science teams to identify the highest-impact opportunities, break down complex problems, and drive high-quality execution.
  5. Communicate roadmap direction, execution progress, and key risks clearly to leadership and partner teams.

Skills

Required

  • Demonstrated ability to independently own and drive complex product areas from strategy through execution.
  • Bachelor's degree or equivalent
  • Strong product instincts, structured thinking, and ability to work through technical ambiguity with engineering and cross-functional partners.
  • Experience defining product requirements, prioritizing roadmaps, and delivering meaningful outcomes in technical product environments.
  • Ability to operate with urgency, adaptability, and high ownership in fast-moving environments.

Nice to have

  • Experience working on products in autonomous vehicles, robotics, AI/ML industries or related technical domains.
  • Domain expertise in at least one of the following pillars: sensor & compute integration, AI & ML systems, or evaluation & validation engines.
  • Comfort operating across software and hardware-adjacent problem spaces.

What the JD emphasized

  • hardest problem in AV today
  • unlocking real-world, long-tail driving data
  • data race
  • technically complex
  • strategically important
  • technical ambiguity

Other signals

  • own a major product area and drive end-to-end product development
  • synthesize complex technical constraints into clear product requirements
  • drive zero-to-one platform builds
  • work closely with engineering, science, operations, and partner teams
  • define what gets built, why it matters, and how success is measured