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 is 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.

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.

What You Will Do

• Design and develop frontier techniques for autonomous vehicle research.
• Lead the end-to-end model lifecycle and system architecture—from data and training through evaluation, deployment, and monitoring—across upstream and downstream dependencies.
• Provide technical leadership through design reviews, mentorship, and hands-on execution.
• Collaborate closely with engineering teams and product teams across AV Labs.
• Publish and present original research at top-tier CV and ML conferences.

Basic Qualifications

• PhD or MS with equivalent industry experience in Computer Science,  Robotics, or a related field.
• 3+ years of relevant industry experience in the field of autonomous vehicles and/or computer vision. 
• Strong understanding of how to connect cutting-edge modeling techniques to autonomous vehicles research in a scalable and reliable way.
• Demonstrated technical contributions, such as publications at leading ML and CV conferences (e.g., CVPR, ICCV, ICLR, NeurIPS) or comparable technical outputs.
• Proficiency in Python and modern deep learning frameworks such as PyTorch or TensorFlow.
• Strong communication skills, with experience providing technical leadership through mentorship, design reviews, and cross-team collaboration.

Preferred Qualifications

• 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.

Responsibilities

For Sunnyvale, CA-based roles: The base salary range for this role is USD $202,000 per year - USD $224,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.

Ready to Ride?

This isn't the kind of place where you follow a playbook — it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves — we'd love to hear from you.

You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.

Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.