Applied Scientist, Safe RL, Robotics, SAF Lab

We are seeking an Applied Scientist to join the SAF Lab, focusing on safe reinforcement learning (RL) for legged locomotion. You will develop RL algorithms that internalize safety and are deployable on physical hardware, enabling robots to walk, run, avoid collisions, and recover from disturbances with agility and robustness.

Responsibilities

• Collaborate with product teams and science leaders to set a science roadmap with impact on real robots.
• Design, train, and deploy reinforcement learning (RL) policies for dynamic legged locomotion, including walking, running, stair climbing, and fall recovery on physical robots.
• Develop sim-to-real transfer pipelines that produce robust policies, including domain randomization, system identification, and adaptive strategies.
• Integrate control-based methods with RL, such as dynamic retargeting, control-guided rewards, safety constraints in training, and safety layers at runtime.
• Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments, domain randomization, and GPU parallelization.
• Investigate the distillation of locomotion policies, integration with whole-body control, foundation models, VLAs, world models, perception, and full-stack autonomy.
• Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis.
• Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in advanced robotics.
• Collaborate with perception and planning teams to enable terrain-aware and goal-conditioned locomotion behaviors.

Requirements

• PhD in Computer Science, Robotics, Mechanical Engineering, 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 patents or publications at top-tier peer-reviewed conferences or journals.

Preferred Qualifications

• Experience in professional software development.
• Knowledge of safety-critical control, including control barrier functions and safety filters.
• Familiarity with safety-constrained RL (e.g., constrained MDPs, Lagrangian methods, shielding, CBF-based policy filtering).
• Experience with model-based control (MPC, whole-body QP controllers, operational space control) and how to interface these methods with RL.
• Knowledge of stability theory (Lyapunov methods, orbital stability) as it applies to periodic gaits.
• Experience with hierarchical RL, skill composition, distillation, and multi-task policy architectures for locomotion.
• Familiarity with real-time deployment constraints (latency budgets, onboard compute limitations, control-loop frequencies).
• Experience building or contributing to large-scale RL training infrastructure (distributed training, GPU clusters).
• Strong communication skills and ability to work across disciplinary boundaries (ML, controls, mechanical engineering).

About The Team

Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab, the first industry research lab in safe autonomy. The lab develops a universal safety layer for the next generation of robotic systems, including mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. You will push the frontiers of performant safety for highly dynamic robots, integrating CBF theory with perception and learning, evaluated on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale.

Benefits

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Pay

USA, CA, PASADENA - $142,800.00 - $193,200.00 USD annually.