Applied Reinforcement Learning Engineer, Whole Body Controls, Optimus

What to Expect

Tesla SI is solving robust embodied intelligence through humanoid robots. On the Whole Body Controls team, you will develop the policies and controllers that let Optimus walk, dance, balance, recover from disturbances, and manipulate objects in real-world settings — the layer of the motion stack that turns high-level intent into physically robust motion on real hardware.

This role is for engineers who come from a controls or robotics background and have applied reinforcement learning to control problems. Most importantly, the motion policies you deploy will be repeatedly shipped to and used by thousands of humanoid robots in real-world applications. We hire at all levels, including recent graduates with strong controls or robotics fundamentals and a demonstrated project or research record.

What You’ll Do

• Develop end-to-end reinforcement-learning policies for whole-body movements, spanning locomotion and manipulation
• Design observations, actions, and rewards based on physics first principles
• Develop techniques to improve sim2real transfer, including classical modeling, domain randomization, and learned dynamics models
• Define the metrics that quantify controller performance, and evaluate policies both in simulation and on hardware
• Ship production-quality policies to a fleet of bots

What You’ll Bring

• Proficiency in Python (numpy, pytorch)
• Solid understanding of robotics fundamentals, including geometry, linear algebra, kinematics, dynamics, probability, and statistics
• A controls or robotics background, with hands-on experience applying reinforcement learning to control problems
• Experience working with robotic systems, ideally legged robotic systems with high degrees of freedom
• Experience with sim2real techniques or a good understanding of physics fundamentals
• Familiarity with state estimation and proprioceptive sensing (e.g. IMU) is a plus
• Experience with C++ and production / real-time software is a plus
• Preferred: hands-on experience deploying and running controllers on real robot hardware; experience with massively parallel simulation (e.g. mjlab, Isaac Lab)
• Equivalent experience through projects, research, or coursework is acceptable