Applied Scientist, Safe RL, Robotics, SAF Lab

Amazon Science; Pasadena (CA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Science's SAF Lab is seeking an Applied Scientist to advance safe reinforcement learning for legged robots. You will develop policies for walking, running, climbing, and fall recovery on real hardware with safety constraints.

You will build sim-to-real pipelines, integrate with safety filters and whole-body control, and collaborate with perception, planning, and controls teams in Pasadena. Expect publishing and broad impact.

Qualifications

• PhD in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering or related field with RL focus.
• Experience applying RL to physical robotic systems and sim-to-real transfer.
• Strong understanding of legged robot dynamics and whole-body control.

Responsibilities

• Collaborate with product teams and science leaders to set a science roadmap with real robot impact.
• Design, train, and deploy RL policies for dynamic legged locomotion on physical robots.
• Develop sim-to-real transfer pipelines robust to reality gaps via domain randomization and system identification.
• Integrate control-based methods with RL, including safety constraints and safety layers.
• Develop and maintain large-scale training infrastructure for locomotion policy learning.
• Investigate distillation, foundation models, and integration with perception and planning.
• Evaluate policy performance through simulation benchmarks and hardware experiments.
• Publish research at top robotics and ML venues and contribute to Amazon's scientific reputation.

Skills

Python

Deep learning

Reinforcement learning

Robotics

Sim-to-real

Education

PhD in Computer Science, Robotics, Mechanical Engineering or Electrical Engineering (RL focus)

Tools

Isaac Gym/Sim

MuJoCo

PyBullet

Job description

Description

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:

• Medical, Dental, and Vision Coverage


• Maternity and Parental Leave Options


• Paid Time Off (PTO)


• 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.

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, 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.

Basic Qualifications

• 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)

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)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants

Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company's reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

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