Reinforcement Learning Engineer, Whole Body Control

We are looking for a Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot.

Responsibilities:

Develop, train, and deploy reinforcement learning algorithms for whole body control.

Determine the observations, actions, and model types that unlock maximum performance.

Identify and close the most important sim-to-real gaps.

Define, test, and evaluate performance metrics for learned policies.

Harden the control stack to ensure rock solid robustness.

Requirements:

Strong background in dynamics and control, ideally of legged robots.

Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc.

Experience tuning hyperparameters and cost functions for these RL algorithms.

Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.

Capable of leading complex controls projects and mentoring junior engineers.

Bonus Qualifications:

Experience with behavior cloning techniques (e.g. distillation).

The US base salary range for this full-time position is $150,000 to $350,000 per year.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.