Applied Scientist, Safe RL, Robotics, SAF Lab

About the role

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

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)
• 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, oper

Additional Information

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. Key job 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 A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you're passionate about this role and want to make an impact on a global scale, please apply!

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at Amazon.com Services LLC? Share your experience