Applied Scientist, Safe Control, Amazon Robotics, Compass

Saved job description

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. You will ensure they don't just work in theory but perform reliably on real robots under real-world conditions. You will push the boundaries of concepts central to CBFs: 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. A key challenge of this role is bridging the gap between the mathematics of set invariance and the realities of hardware, including sensor noise, model uncertainty, computational budgets, and discrete state transitions. You will ensure that these algorithms are not only provably correct but also implementable within a safety-critical architecture that must be certified by a third-party. You will contribute directly to the next generation of CBF theory and its practical deployment across Amazon's diverse robot fleet.

Key job 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

A day in the life

Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:

1. Medical, Dental, and Vision Coverage

2. Maternity and Parental Leave Options

3. Paid Time Off (PTO)

4. 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

About the team

Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

Requirements: - 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)