Reinforcement Learning Engineer, Whole Body Control

Highlights

• Develop advanced reinforcement learning for whole-body humanoid control.
• Ship general-purpose humanoids that work in homes and commercial spaces.
• Define metrics and harden control stack for rock-solid robustness.

Ideal candidate

A controls-oriented roboticist who loves taking learned locomotion from sim to real hardware and leading complex projects. You care about robust humanoid performance and mentoring others to advance human-level intelligence.

Requirements

• Strong background in dynamics and control, ideally of legged robots
• Experience with reinforcement learning algorithms for robotics such as PPO, SAC
• Experience tuning hyperparameters and cost functions for RL algorithms
• Familiarity with domain randomization, curriculum learning, reward shaping
• Able to lead complex controls projects and mentor junior engineers