Applied Scientist, Safe RL, Robotics, SAF Lab

Amazon•Pasadena, CA

6d•$142,800 - $193,200•Onsite

About The Position

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. Therefore, your work will sit at the intersection of safety-critical control and learning, and you will collaborate with others in the SAF Lab and Amazon working on perception, planning, whole-body and safety-critical control. This is an opportunity to shape the foundations of safe learning on emerging platforms that will remove bottlenecks to deployment and enable these robots to safely operate around humans.

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)

Nice To Haves

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)

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 in training), 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
sign-on payments
restricted stock units (RSUs)
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
401(k) matching
paid time off
parental leave