Applied Scientist, Safe Control, Robotics, Compass

We are seeking an Applied Scientist to join Compass. In this role, you will develop the core Control Barrier Function (CBF) algorithms that form the mathematical foundation of the Compass safety system, ensuring they perform reliably on real robots under real‑world conditions. You will push the boundaries of concepts central to CBFs—including computing robust invariant sets, designing hybrid system formulations that handle contact transitions and mode switches, and developing backup‑set approaches that leverage learned policies and multiple controllers—while bridging the gap between advanced theory and the realities of hardware such as sensor noise, model uncertainty, and computational limits.

• Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope for each robot platform.
• Compute and refine invariant sets for complex, high‑dimensional robotic systems, developing scalable methods beyond existing analytical approaches.
• Design formulations for hybrid dynamical systems, handling discrete mode transitions (e.g., contact/no‑contact, stance/flight phases) with provable safety across switching boundaries.
• Address the theory‑to‑practice gap by developing methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency.
• Create reduced‑order and full‑order dynamics models with both white‑box and black‑box approaches.
• Implement real‑time optimization solvers that execute within the tight timing budgets of safety‑critical control loops.
• Develop formal arguments and documentation sufficient to support third‑party safety certification of algorithms.
• Validate algorithms through rigorous simulation and hardware experiments, characterizing failure modes and quantifying safety margins.
• Contribute to the theoretical foundations of CBFs through publications at top‑tier controls and robotics venues.
• Collaborate with perception, planning, locomotion, and manipulation teams to accommodate the needs of upstream and downstream systems.

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You will integrate safety with motion planning, reinforcement learning, and foundation models, ensuring safety never blocks robot performance. Your work will underpin robots operating alongside people at Amazon’s unprecedented scale.

• PhD, or Master’s degree with 4+ years of experience in deep learning, computer vision, human‑robot interaction, or algorithm implementation.
• Deep expertise in Control Barrier Functions, including theoretical foundations and practical implementation.
• Strong mathematical background in dynamical systems theory, nonlinear control, and formal verification or reachability analysis.
• Proficiency in C++ and Python with experience implementing control algorithms for real‑time systems.
• Publication record at relevant venues (e.g., CDC, ACC, ICRA, RSS, Automatica, TAC).

• Experience in professional software development.
• Experience validating safety‑critical algorithms on physical robotic hardware.
• Experience with hybrid systems theory and formulations that handle discrete transitions.
• Experience with robust or adaptive methods that account for parametric uncertainty or unmodeled dynamics.
• Knowledge of functional safety standards (IEC 61508, ISO 13849, ISO 26262) and experience preparing algorithms for third‑party certification.
• Familiarity with real‑time embedded systems and the constraints of deploying optimization‑based controllers on safety‑rated hardware.
• Experience formulating and solving optimization‑based controllers (QPs, SOCPs) for real‑time safety filtering.

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