Staff/Lead Machine Learning Engineer, Behavior & Planning

Responsibilities

Technical Leadership

• Lead machine learning initiatives across the autonomy stack, framing ambiguous problems, setting technical direction, and driving projects from conception through on-road deployment.
• Design, train, and productionize state-of-the-art models for foundation and world models, LLM/VLM reasoning, reinforcement and imitation learning, generative and diffusion models, and transformer-based prediction and planning.
• Build behavior and planning models that generalize to new cities, geographies, and vehicle platforms.
• Own the full model lifecycle, including data collection, training, onboard inference, closed-loop and open-loop evaluation, and continuous on-road iteration.
• Partner with Perception, Simulation & Evaluation, ML Infra & Data, Autonomy, and Research teams to develop solutions, align priorities, and unblock shared initiatives.
• Mentor engineers and researchers, raise the team’s technical bar, and shape broader roadmap and technical strategy.

Requirements

• 7+ years building and deploying machine learning systems, including leading complex, multi-team technical initiatives.
• Master’s or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field, or equivalent practical experience.
• Strong general machine learning foundations, including model architectures, training, and evaluation.
• Depth in one or more of sequential decision-making, prediction, generative modeling, foundation/world models, or representation learning.
• Understanding of the complete machine learning development cycle, from data collection and training through deployment, onboard inference, and data iteration loops.
• Strong programming and problem-solving skills in Python and/or C++.
• Ability to lead initiatives, collaborate across teams, and work independently with minimal oversight.
• Clear communication skills and the ability to align cross-functional stakeholders.

Nice-to-Haves

• Experience in autonomous vehicles or robotics, with familiarity with machine learning in an autonomy or planning stack.
• Experience mentoring engineers or leading a team, with interest in a management or technical-leadership track.
• Familiarity with PyTorch, JAX, or TensorFlow.
• Research contributions to venues such as NeurIPS, ICLR, ICML, CVPR, RSS, or CoRL.
• Strong applied machine learning and production impact.

Compensation and Benefits

• Base pay range: $235,030–$352,290.
• Eligibility for an annual performance bonus, equity, and a competitive benefits package.