Machine Learning Engineer II - AV Foundation, AV Labs

Machine Learning Engineer II - AV Foundation, AV Labs Department: Engineering Team: Machine Learning Location: Sunnyvale, 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. You will be an ML engineer in AV Labs conducting autonomous vehicle modeling and research, with an emphasis on developing foundational and applied techniques that shape the future of autonomous driving. The ideal candidate will formulate research problems, design and evaluate novel modeling approaches, and influence the direction of large-scale autonomous systems through strong scientific judgment and execution. You will work cross-functionally with engineers from AV Labs and partner engineering teams which enables Uber’s mission of helping people go anywhere and get anything and earn their way. **What You Will Do:** 1. Design and implement cutting-edge techniques for autonomous vehicle systems. 2. Collaborate closely with engineering teams and product teams across AV Labs to integrate modeling approaches into production pipelines. 3. Publish and present original research at top-tier CV and ML conferences. **Basic Qualifications:** 1. PhD or MS with equivalent industry experience in Computer Science,  Robotics, or a related field. 2. Relevant experience in autonomous vehicles or computer vision, with a strong understanding of how research ideas connect to autonomous vehicle systems. 3. Demonstrated technical contributions, including publications a