Lead Product Manager, AV Labs

Lead Product Manager, AV Labs Department: Product Team: Product Management Location: San Francisco, United States Type: Full-Time **About the Role** Uber is launching AV Labs to accelerate the autonomous technology ecosystem. We’re building out a high-velocity team of multi-disciplinary experts to turn real-world operations into high-quality data for our autonomous partners. This team will be focused on the hardest problem in AV today: unlocking real-world, long-tail driving data. Autonomy is now a data race–and Uber has an edge: We collect rare, real-world driving data at a scale and capital efficiency no one else can match (millions of Uber trips every hour across cities, conditions, and edge cases create the data autonomy has been missing). We will build platforms that harness scale and real-world complexity to reimagine how the world moves. As a Lead Product Manager on AV Labs, you will own a major product area and drive end-to-end product development for initiatives that are both technically complex and strategically important. You will synthesize complex technical constraints into clear product requirements and drive zero-to-one platform builds by working closely with engineering, science, operations, and partner teams to define what gets built, why it matters, and how success is measured. This role is ideal for a PM who thrives in ambiguity, can build conviction from incomplete information, and can drive execution across multiple stakeholders in a rapidly evolving technical environment. **What the Candidate Will 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-impa