Applied Scientist, Safe RL, Robotics, SAF Lab

Job description

We are seeking an Applied Scientist to join the SAF Lab. In this role, you will lead the effort in safe reinforcement learning (RL) including the development of legged locomotion algorithms that internalize safety and are deployable on physical hardware—enabling highly dynamic robots to walk, run, avoid collisions and recover from disturbances with agility and robustness. You will develop RL architectures that interface with physics-based models (for dynamic retargeting and reward shaping), internalize safety constraints in training, sim-to-real transfer and interface with safety filters at run-time.

Key job responsibilities • Collaborate with product teams and science leaders to set a…

Job requirements

Experience in patents or publications at top-tier peer-reviewed conferences or journals
PhD in Computer Science, Robotics, Mechanical Engineer, Electrical Engineering, or a related field with a focus on reinforcement learning, robot learning, or control
Experience applying RL to physical robotic systems (beyond simulation-only work), including demonstrated expertise in sim-to-real transfer on dynamically stable robots
Strong understanding of legged robot dynamics, contact mechanics, and whole-body control fundamentals
Proficiency in Python and deep learning frameworks (e.g., PyTorch, JAX) with experience building custom RL training pipelines
Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet)
Experience in professional software development
Knowledge of safety-critical control, including control barrier functions and safety filters.

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Applied Scientist, Safe RL, Robotics, SAF Lab

Job description

Job requirements

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