Postdoctoral Scholar - SAF Lab, Compass

Job :  |  Services LLC

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.

• Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains.
• Develop the simulation and evaluation pipelines needed to run complex and large‑scale validation of methods developed in high‑fidelity simulation environments.
• Develop sim‑to‑real transfer pipelines that enable the deployment of simulation‑based methods (controllers, policies) on hardware.
• Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research.
• 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 set a science roadmap (with eventual impact on real robots).

• 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, Mu Jo Co , 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).

• 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…