Applied Scientist, Safe Control, Amazon Robotics, Compass

Amazon Amazon · Big Tech · Pasadena, CA · Applied Science

This role focuses on developing and implementing Control Barrier Function (CBF) algorithms for Amazon's robot fleet, ensuring safety and reliability in real-world conditions. The scientist will bridge the gap between theoretical mathematics and practical hardware implementation, addressing challenges like sensor noise and computational budgets, with the goal of achieving third-party safety certification.

What you'd actually do

  1. Develop and implement novel CBF algorithms that provide formal safety guarantees while minimizing conservatism to maximize the permissible operating envelope for each robot platform
  2. Compute and refine invariant sets for complex, high-dimensional robotic systems, developing scalable methods that go beyond what existing analytical approaches can handle
  3. Design formulations for hybrid dynamical systems, handling discrete mode transitions (e.g., contact/no-contact, stance/flight phases) with provable safety across switching boundaries
  4. Address the theory-to-practice gap by developing methods that are robust to model uncertainty, sensor noise, actuation delays, and computational latency
  5. Create reduced-order and full-order dynamics models with both white-box and black-box approach

Skills

Required

  • PhD, or Master's degree and 4+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience
  • 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)

Nice to have

  • Experience in professional software development
  • Experience validating safety-critical algorithms on physical robotic hardware (not simulation-only)
  • Experience with hybrid systems theory and formulations that handle discrete transitions (e.g., contact events, mode switches)
  • 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

What the JD emphasized

  • 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
  • Publication record at relevant venues (e.g., CDC, ACC, ICRA, RSS, Automatica, TAC)
  • Experience validating safety-critical algorithms on physical robotic hardware (not simulation-only)
  • Knowledge of functional safety standards (IEC 61508, ISO 13849, ISO 26262) and experience preparing algorithms for third-party certification

Other signals

  • Develop and implement novel CBF algorithms that provide formal safety guarantees
  • Ensure algorithms are not only provably correct but also implementable within a safety-critical architecture that must be certified by a third-party
  • Contribute directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet