Applied & Research Scientist - Safe Autonomy Frontiers (SAF) Lab, Safe Autonomy Frontiers (SAF) Lab

• Key job responsibilities
• -Advance the science of safe autonomy from formal foundations to the integration with learning and perception to validation on hardware — with particular emphasis on methods that bridge these domains.
• -Test and validate hardware, with a focus on next generation robotic systems. Including locomotion, manipulation and loco-manipulation.
• -Publish research at top-tier robotics, control, and ML venues, and contribute to Amazon’s scientific reputation in advanced robotics.
• -Collaborate with SAF Lab and Amazon production teams to move research into robots deployed at Amazon scale.
• A day in the life
• About the team
• PhD or equivalent research experience
• Master’s degree and 3+ years of applied science, research, or robotics engineering experience — OR a PhD — in computer science, robotics, control, mechanical engineering, electrical engineering, or a related field.
• Hands-on experience developing and deploying control algorithms and/or learning policies on physical robotic hardware in a research or production setting (not simulation-only).
• Working knowledge of safety-critical control, including control barrier functions and safety filters.
• Proficiency in C++ and Python, with a track record of implementing control algorithms and/or learning policies in real systems.
• Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet).
• Demonstrated technical impact through shipped systems, patents, or publications at top-tier venues (e.g., CDC, ACC, L-CSS, ICRA, IROS, RSS, RA-L, Automatica, TAC, TRO).
• Experience in professional software development, including production code quality, testing, and deployment practices.
• Experience taking robotics or autonomy research from prototype to deployed product, including collaboration with hardware, product, and operations teams.
• Understanding of autonomous systems, reduced-order models, layered control architectures, nonlinear control, reachability methods, legged locomotion, and whole-body control.
• Knowledge of learning-based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models).
• Exposure 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 focus on layered architecture in robotic systems (high-level planning, mid-level trajectory generation, low-level feedback control).
• Experience with perception on robotic systems (e.g., depth-camera and LiDAR-based sensing, sensor fusion, semantic tagging), together with mapping and navigation.
• 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).

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