Postdoctoral Scholar - SAF Lab, Compass

Amazon • Pasadena, CA

1d • $136,000 - $184,000 • Onsite

About The Position

Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab, the first industry research lab in safe autonomy. This lab is developing a universal safety layer for the next generation of robotic systems, including mobile robots, manipulators, mobile manipulators, and future platforms with dynamic stability. The role involves pushing the frontiers of performant safety for highly dynamic robots, integrating CBF theory with perception and learning, and evaluating these on next-generation robots. The work will underpin robots operating alongside people at Amazon's scale. The SAF lab offers a unique opportunity to collaborate with the inventor of CBFs, top scientists and engineers at Amazon, and establish connections with top academic research labs. The research will lay the foundations of safe learning on complex robots, removing deployment bottlenecks and enabling safe operation around humans.

Requirements

• PhD in Computer Science, Robotics, Control, Mechanical Engineering, Electrical Engineering, or a related field with a focus on control, learning, and/or robotics.
• Deep understanding of safety-critical control, including control barrier functions and safety filters.
• Proficiency in C++ and Python with experience implementing control algorithms and/or learning policies.
• Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet).
• Experience validating on physical robotic hardware (not simulation-only).
• Track record of publications at top-tier venues in control and robotics (e.g., RSS, ICRA, IROS, CDC, CoRL, NeurIPS, ICLR, L-CSS, RAL, TRO, TAC).

Nice To Haves

• Understanding of locomotion, reduced order models, layered control architectures, nonlinear control, reachability methods, and whole-body control.
• Knowledge of learning-based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models).
• Exposure to learning-based approaches for CBF synthesis (e.g., neural CBFs, data-driven barrier functions) and the integration of CBFs into learning (e.g., CBF-RL).
• Understanding of control systems engineering, with a specific focus on layered architecture used in robotic systems (high level planning, mid-level trajectory generation and low-level feedback control).
• Experience with perception on robotic systems (e.g., depth camera and LiDAR based sensing modalities, sensor fusion, semantic tagging).
• Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches.
• Knowledge of 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/or simulation-based predictive control (MPPI).
• 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

• Push forward the fundamental science of safe autonomy through theoretical contributions, integration with learning, or synthesis from perception, especially methods that bridge these domains.
• Develop simulation and evaluation pipelines for complex, large-scale validation of methods in high-fidelity simulation environments.
• Develop sim-to-real transfer pipelines for deploying simulation-based methods (controllers, policies) on hardware.
• Deploy developed methods on hardware, focusing on dynamically stable robots, validating the underlying science in practice, and identifying gaps to drive innovation.
• Publish research at top-tier robotics, control, and ML venues to contribute to Amazon's scientific reputation in advanced robotics.
• Collaborate with product teams and science leaders to set a science roadmap with eventual impact on real robots.

Benefits

• 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
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree