ML Engineer, Motion Planning

What you'll do

• Build and evaluate ML-powered driving behavior, scene-understanding, and trajectory-planning systems, including hybrid approaches that combine learned and traditional planning.
• Train and deploy large-scale models under onboard compute, latency, and safety constraints; build scenario-mining, simulation, evaluation, and iteration infrastructure.
• Collaborate across engineering teams to investigate failures and improve autonomous-driving performance.

What they ask for

• B.S., M.S., or Ph.D. in a related technical field, or equivalent practical experience.
• 3+ years of industry experience for Senior, or 5+ years for Staff, in machine learning, autonomous driving, robotics, or related fields.
• Production ML model development and deployment experience; proficiency in Python and C++, and hands-on experience with modern deep learning frameworks.
• Experience applying machine learning to motion planning, robotics, or related decision-making problems, and foundations in linear algebra, control theory, or motion planning.

Nice to have

• Experience with closed-loop simulation, large-scale evaluation, and data-driven model iteration for autonomous driving.
• Experience deploying and optimizing real-time onboard inference, including TensorRT, CUDA, quantization, and compute or latency optimization.

Pay

$169,783–$282,264 USD base salary; $203,726–$338,694 USD base salary

Tools

Python, C++, PyTorch, JAX, TensorFlow, TensorRT, CUDA

AI focus

Develop learned planners, end-to-end models, and other ML systems for autonomous driving, including scene understanding, decision-making, and trajectory generation.