Applied Scientist, Safe Control, Amazon Robotics, Compass
Core
Develop Control Barrier Function (CBF) algorithms to ensure safe, reliable operation of robots in real-world conditions, bridging mathematical theory with hardware implementation.
Role type
Applied Scientist (Safe Control)
Builds
Core safety algorithms for Amazon's robot fleet (mobile robots, manipulators, mobile manipulators)
Domain
Robotics, Control Theory, Safety-Critical Systems
Deliverable
production ML models | product features
Required skills
Control Barrier Functions (CBF), dynamical systems theory, nonlinear control, formal verification, C++, Python, real-time optimization solvers, hybrid systems theory, reduced-order/full-order dynamics modeling, QPs/SOCP formulation
Preferred skills
Professional software development, safety-critical hardware validation, functional safety standards (IEC 61508, ISO 13849, ISO 26262), real-time embedded systems, adaptive methods for parametric uncertainty
Technologies
C++, Python, QPs, SOCPs
Responsibilities
Develop and implement novel CBF algorithms for formal safety guarantees; Compute and refine invariant sets for high-dimensional robotic systems; Design formulations for hybrid dynamical systems handling discrete mode transitions; Address theory-to-practice gaps regarding model uncertainty and sensor noise; Create dynamics models using white-box and black-box approaches; Implement real-time optimization solvers within tight timing budgets; Develop documentation for third-party safety certification; Validate algorithms via simulation and hardware experiments; Contribute to CBF theory through publications; Collaborate with perception, planning, and locomotion teams
Seniority
Senior, hands-on IC
