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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 frontier research for autonomous vehicles. The ideal candidate will formulate research problems, design and evaluate novel modeling approaches, and influence the direction of autonomous vehicles research through strong scientific judgment and execution. You will work cross-functionally with engineers from AV Labs and partner engineering teams.
Job Responsibility
Design and develop cutting-edge techniques for autonomous vehicle research
Collaborate closely with engineering teams and product teams across AV Labs
Publish and present original research at top-tier CV and ML conferences
Requirements
PhD or MS with equivalent industry experience in Computer Science, Robotics, or a related field
Relevant experience in the field of autonomous vehicles or computer vision, with a strong understanding of how research ideas connect to autonomous vehicles
Demonstrated technical contributions, including publications at leading ML and CV conferences such as CVPR, ICCV, ICLR, NeurIPS, or similar venues
Proficiency in Python and modern deep learning frameworks such as PyTorch or TensorFlow
Strong communication skills and the ability to collaborate effectively with researchers and engineers across teams
Nice to have
Experience with autonomous vehicle research
Hands-on experience in training multimodal LLMs for autonomous vehicles or relevant computer vision systems
Recent publications in one or more of the following areas: 3D perception & scene understanding, multimodal embedding, diffusion models, gaussian splatting, vision language action models, and world models