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Tesla

Industry:
Humanoid Robotics
Location:
Job Type:
Software
Reinforcement Learning Engineer, Whole Body Controls, Optimus
Job Description:

Tesla AI is solving robust embodied intelligence through humanoid robots. On the Whole Body Controls team, you will develop the policies that let Optimus walk, 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 with a strong reinforcement-learning foundation who may be new to robotics: we will teach you the physics and control, and you will bring the reinforcement learning expertise that lets the robot learn to move. Most importantly, the 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 a strong reinforcement learning foundation and a demonstrated project or research record.

Job Responsibilities:
  • Develop end-to-end reinforcement-learning policies for whole-body control spanning locomotion and manipulation: walking, balancing, disturbance recovery, and manipulating objects
  • Design reward functions, action and observation spaces, and curricula; tune and scale reinforcement-learning training
  • Improve the sample efficiency, exploration, stability, and sim2real robustness of learned policies
  • Build large-scale, simulation-based training pipelines
  • Evaluate policies both in simulation and on hardware, and ship them to a fleet of bots
Job Requirements:
  • Proficiency in Python (numpy, pytorch)
  • Strong reinforcement-learning fundamentals, including policy-gradient and actor-critic methods, with hands-on experience training reinforcement-learning agents
  • Curiosity about physics and robotics and an eagerness to learn control and dynamics on the job; no prior robotics experience required
  • Preferred: large-scale or distributed reinforcement learning and massively parallel simulation (e.g. mjlab, IsaacLab); reward shaping, curriculum learning, or sim2real; any robotics or control exposure
  • Equivalent experience through projects, research, or coursework is acceptable
Apply on Tesla Website
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