Robotics Autonomy Engineer, Locomotion

What you'll do

• Build reinforcement-learning pipelines, policy architectures, training curricula, and simulation workflows for locomotion and whole-body control.
• Train and optimize locomotion policies using human motion data, imitation learning, and sim-to-real methods.
• Validate robot behaviors on hardware and in the field, and improve performance using telemetry and field data.

What they ask for

• Master’s degree or higher in Robotics, Computer Science, Engineering, or a related field.
• Deep expertise in reinforcement learning for continuous control and hands-on experience with legged robot platforms.
• Proficiency with simulation tools such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet; strong understanding of sim-to-real transfer, contact dynamics, control theory, and kinematics.
• Strong Python and/or C++ development skills in Linux-based environments.

Nice to have

• PhD strongly preferred; 2+ years developing and deploying locomotion policies on real robotic systems is stated as preferred.
• Experience in an industry or startup robotics setting; experience with resource-constrained deployment, ROS/ROS2, or motion imitation pipelines.

Pay

USD 100,000–230,000 (per-year-salary), base pay

Tools

Isaac Gym, Isaac Lab, MuJoCo, PyBullet, Python, C++, PyTorch, JAX

AI focus

Reinforcement learning for continuous-control locomotion and whole-body coordination, imitation learning from human motion, policy distillation, and sim-to-real deployment on legged and humanoid robots.