Job Summary:
Tesla is focused on solving robust embodied intelligence through humanoid robots, and they are seeking a Reinforcement Learning Engineer to develop cutting-edge policy learning algorithms. The role involves creating end-to-end reinforcement-learning policies for whole-body movements and evaluating these policies in both simulation and real-world settings.
Responsibilities:
• 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
Qualifications:
Required:
• 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
Preferred:
• Experience implementing control strategies including impedance control, adaptive control, force control, MPC on hardware
Company:
Tesla is an electric vehicle and clean energy company that provides electric cars, solar, and renewable energy solutions. Founded in 2003, the company is headquartered in Austin, USA, with a team of 10001+ employees. The company is currently Late Stage.