Senior Reinforcement Learning Engineer

Highlights

• Push Apollo humanoid to world-class locomotion and manipulation on real hardware.
• Drive full cycle from simulation prototyping to robust transfer on physical robots.
• Translate human mocap and teleoperation into robust reference trajectories.

Ideal candidate

A hands-on roboticist obsessed with seeing learning work on real hardware. You love rapid iteration, system-level problem solving, and mentoring others to push humanoids further.

Requirements

• 5+ years hands-on expertise with RL frameworks (PyTorch, JAX) and physics simulators (MuJoCo, IsaacGym)
• Mastery of Python for prototyping and strong proficiency in C++ for deployable code
• Experience building or using large-scale distributed training pipelines
• Deep theory in modern RL including imitation learning, model-based RL and sim-to-real transfer
• Intuition for robot dynamics and controls to guide learning-based approaches
• PhD or MS in Computer Science, Robotics or related field with 2+ years industry experience preferred
• Proven track record deploying learning-based policies on physical robots, especially legged robots or manipulators
• Demonstrated experience mentoring engineers