Applied Scientist, Safe Control, Amazon Robotics, Compass
Pubblicato il 19-10-2026 - Amazon in Asti
Applied Scientist, Safe Control, Amazon Robotics, Compass Job ID: 10576418 | Services LLC
È questo il suo prossimo lavoro? Legga la descrizione completa qui sotto per scoprirlo e non esiti a candidarsi.
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, actuatio