Reinforcement Learning Engineer, Policy, Optimus

Tesla AI is solving robust embodied intelligence through humanoid robots. The goal of our reinforcement learning team is to build and demonstrate a general robot learning system that can leverage AI to perform complex physical tasks, ranging from full body locomotion, precise manipulation, and more. Our reinforcement and imitation learning engineers are responsible for end-to-end robotic learning and own this stack from inception to deployment. Most importantly, you will see your work repeatedly shipped to and utilized by thousands of humanoid robots in real world applications.

Responsibilities

Develop end-to-end robotic learning with either reinforcement or imitation learning Reinforcing correct set of actions, rewarding correct behavior and negating incorrect behavior (with real-time action/reward feedback loops) Perform a large number of instructions and generalize new tasks with different objects and environments Learn to perform dexterous tasks using high degree of freedom hands Learn different robot policies to solve language-conditioned tasks from vision Ship production quality, safety-critical software

Requirements

Experience in end-to-end robotic learning, with either imitation or reinforcement learning Experience writing production-level Python (including Numpy and Pytorch) Experience with distributed deep learning systems Exposure to robot learning through tactile and/or vision-based sensors is a plus Proven track record of training and deploying real world neural networks