Senior Applied Scientist, Safe Locomotion, Compass

Amazon•Pasadena, CA

6d•$167,100 - $226,100•Onsite

About The Position

We are seeking an Applied Scientist to join Amazon Robotics, Compass Team. In this role, you will own the development of safe legged locomotion algorithms and their deployment on physical hardware, developing learning-based controllers that enable quadrupeds and humanoids to walk, run, and recover from disturbances with agility and robustness. You will leverage Reinforcement Learning (RL), sim-to-real transfer, and other learning-based architectures to train policies that produce stable, dynamic gaits across varied terrains and operating conditions. These learned policies will interface with model-based control strategies to form whole-body control laws that balance performance and safety. Your work sits at the novel intersection of safety and machine learning, where these learned policies will be used in a safety-critical context for complex safety constraints like stability. You will collaborate closely with perception, planning, and safety teams to close the loop between what the robot sees, where it needs to go, and how it moves to get there safely. This is a rare opportunity to shape how legged robots move through the world alongside people.

Requirements

PhD, or Master's degree and 6+ years of applied research experience
3+ years of experience applying RL to physical robotic systems (beyond simulation-only work)
Demonstrated expertise in sim-to-real transfer for locomotion or manipulation tasks
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)
Track record of publications at top-tier venues (e.g., RSS, CoRL, ICRA, NeurIPS, ICLR, IROS)

Nice To Haves

Experience deploying RL-trained locomotion policies on physical quadrupeds or humanoids
Familiarity with safety-constrained RL
Experience with model-based control (MPC, whole-body QP controllers, operational space control) and how learned policies compose with them
Knowledge of stability theory (Lyapunov methods, orbital stability) as it applies to periodic gaits
Experience with hierarchical RL, skill composition, or 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

Design, train, and deploy reinforcement learning policies for dynamic legged locomotion including walking, running, stair climbing, and fall recovery on physical quadruped and humanoid platforms
Collaborate with the Compass safety team to ensure locomotion policies operate within safety-critical bounds, incorporating control barrier functions or other formal safety mechanisms as constraints during or after training
Develop sim-to-real transfer pipelines that produce policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies
Integrate learned locomotion policies with model-based whole-body controllers, defining how RL outputs (e.g., joint targets, contact schedules) interface with optimization-based control layers
Formulate reward functions and training curricula that encode both performance objectives and safety constraints, ensuring policies respect stability and contact-force limits
Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments and parallelized training pipelines
Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis
Investigate emerging techniques (e.g., foundation models for control, diffusion policies, world models) and assess their applicability to safe legged locomotion
Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in legged 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