Postdoctoral Scholar - SAF Lab, Compass

About the role

The SAF Lab is the first industry research lab dedicated to safe autonomy, focusing on developing a universal safety layer for next-generation robotic systems. The role involves pushing the frontiers of performant safety for highly dynamic robots through research on control barrier functions (CBFs), safe reinforcement learning, and layered safety filters.

Responsibilities

• Perform research on safe autonomy for highly dynamic robots, with a focus on loco-manipulation and dynamically stable robots.
• Develop simulation and evaluation pipelines for complex and large-scale validation of methods in high-fidelity simulation environments.
• Create sim-to-real transfer pipelines to deploy simulation-based methods on hardware.
• Deploy methods on hardware, particularly dynamically stable robots.
• Validate scientific discoveries in practical applications and identify gaps between theory and practice to drive innovation.
• Publish research at top-tier robotics, control, and ML venues and contribute to Amazon's scientific reputation in advanced robotics.
• Collaborate with product teams and science leaders to establish a science roadmap with potential real-world impact.

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).

Qualifications

• 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).

Skills

• Ability to 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.
• Follow all federal, state, and local laws and Company policies.
• Exercise sound judgment.
• Effectively manage stress and work safely and respectfully with others.
• Exhibit trustworthiness and professionalism.
• Safeguard business operations and the Company’s reputation.

Benefits

• Comprehensive benefits including health insurance, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage.
• 401(k) matching.
• Paid time off.
• Parental leave.