Applied Scientist: Safe Control for Real-World Robotics

Amazon Inc.; Pasadena (CA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Robotics Compass in Pasadena, CA, seeks an Applied Scientist to develop core Control Barrier Function (CBF) algorithms for the Compass safety system. You will ensure robust invariance and safe operation on real robots under real-world conditions, including sensor noise and uncertainties.

Responsibilities include designing hybrid formulations, real-time solvers, and certification-ready documentation while collaborating across perception, planning, locomotion, and manipulation teams.

Qualifications

• 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)

Responsibilities

• 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 that go beyond what existing analytical approaches can handle
• 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 approach
• 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

Skills

Deep learning

Computer vision

Human robot interaction

Algorithms implementation

Control Barrier Functions

C++

Python

Real-time control

Publications

Education

PhD or Master's with 4+ years of relevant experience

Tools

C++

Python

Job description

Amazon Robotics Compass in Pasadena, CA, seeks an Applied Scientist to develop core Control Barrier Function (CBF) algorithms for the Compass safety system. You will ensure robust invariance and safe operation on real robots under real-world conditions, including sensor noise and uncertainties.

Responsibilities include designing hybrid formulations, real-time solvers, and certification-ready documentation while collaborating across perception, planning, locomotion, and manipulation teams.