Tesla AI is seeking a candidate to develop cutting-edge reinforcement-learning policies for humanoid robots, focusing on whole-body movements and real-world applications. The role involves improving sim2real transfer techniques and evaluating policies both in simulation and on hardware.

• Experience writing production-quality python (including numpy and pytorch)
• Solid understanding of robotics fundamentals, including geometry, linear algebra, kinematics, dynamics, probability, and statistics
• Familiarity with Machine learning and Reinforcement Learning fundamentals OR strong background in optimization-based planning and control
• Experience working with robotic systems, ideally on legged robotic systems with high degrees of freedom
• Experience with sim2real techniques OR deep understanding of physics fundamentals
• Experience implementing control strategies including impedance control, adaptive control, force control, MPC on hardware preferred

• Develop end-to-end reinforcement-learning policies for whole-body movements
• Design observations, actions, and rewards based on first principles and deep physics understanding
• Develop techniques to improve sim2real transfer, including classical modeling techniques
• Evaluate policies both in simulation and on hardware
• Ship production-quality policies to a fleet of bots

• Employees at Tesla are often offered day-one coverage with multiple medical options (some at $0 paycheck cost), dental/vision, company HSA contributions, a 401(k) match, and equity programs. Most roles also include paid time off and holidays, family-building support, employee assistance, commuter and childcare benefits, and access to discounts and wellness programs.