Applied Scientist, Safe RL, Robotics, SAF Lab

About the role

We are seeking an Applied Scientist to lead the effort in safe reinforcement learning (RL), including the development of legged locomotion algorithms that internalize safety and are deployable on physical hardware. This role will enable 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, facilitate sim-to-real transfer, and interface with safety filters at runtime. Your work will sit at the intersection of safety-critical control and learning, collaborating with teams in the SAF Lab and across Amazon working on perception, planning, whole-body control, and safety-critical systems.

This is an opportunity to shape the foundations of safe learning on emerging platforms, removing bottlenecks to deployment and enabling robots to safely operate around humans.

Responsibilities

• Collaborate with product teams and science leaders to set a science roadmap with eventual 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 policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies.
• Integrate control-based methods with RL, as inputs to the RL (dynamic retargeting and control-guided rewards), in training (internalizing safety constraints), and as the RL feeds into safety layers and whole-body control.
• 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.

Benefits

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

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 SAF 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 and evaluating on next-generation robots. Your work will underpin robots operating alongside people at Amazon's unprecedented scale.