Applied Scientist, Safe RL, Robotics, SAF Lab

Job Description

Lead the development of safe reinforcement learning algorithms for legged robots to enable agile movement and collision avoidance on physical hardware. Collaborate with cross-functional teams to design sim-to-real pipelines and integrate control-based safety layers into RL architectures.

Requirements: Requires a PhD in Computer Science, Robotics, or a related field with a strong track record of publications in RL and robot learning. Must have demonstrated experience applying RL to physical robotic systems and proficiency in deep learning frameworks and physics simulators.

Key Skills: Safe Reinforcement Learning, Legged Locomotion, Sim-to-Real Transfer, Control Barrier Functions, Python, PyTorch, JAX, Isaac Gym, MuJoCo, PyBullet, Whole-Body Control, Model Predictive Control, Deep Learning, Physics Simulation, Stability Theory, Robot Learning

Benefits: Medical Coverage, Dental Coverage, Vision Coverage, Maternity Leave, Parental Leave, Paid Time Off, 401k Plan, Sign-on payments, Restricted stock units (RSUs), Life Insurance, Employee Assistance Program, Mental Health Support, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement