Research Scientist, Safety-Critical Control, Robotics, SAF Lab

We are seeking a Research Scientist to join the SAF this role you will develop the core Control Barrier Function (CBF) theory and algorithms that form the mathematical foundation of the universal safety layer. Key to this process is a feedback loop between theory and practice: developing theory that is deployed on next generation robots and using experimental evaluation to drive new theory. This will enable you to push the boundaries of CBF theory: layered safety filters and trade-offs between robustness and optimality. A key challenge will be to understand the interplay with CBF theory and learned control policies constructing safety filters that internalize learned policies and utilizing CBFs in learning to internalize safety. You will work with the inventor of control barrier functions and a team contributing directly to the next generation of CBF theory and its practical deployment across Amazons 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 highly dynamic robots
Frame safety filtering within complex layered architectures involving learning-based components including VLAs RL-based locomotion and whole-body controllers
Design multi-layer CBF based safety filters including decision making layers MPC and real-time nonlinear feedback control elements
Formalize the interplay between models used in the CBF safety filter and the full order dynamics of the robotic systems establishing formal guarantees even if the full order system dynamics is not known and contains learning-based elements
Understand the role of perception and semantic representations in the synthesis of CBFs and the interplay between CBFs
Characterize the trade-offs between optimal safety and robustness to sensor noise perception error actuator and sensor failure
Address the theory-to-practice gap by developing CBF methods that are robust to model uncertainty sensor noise actuation delays and computational latency
Implement real-time optimization solvers (e.g. QP-based safety filters) that execute within the tight timing budgets of safety-critical control loops
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 ensure CBF formulations accommodate the needs of upstream and downstream systems
Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots)


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 wed still love to hear from you! At Amazon we value people with unique backgrounds experiences and skillsets. If youre passionate about this role and want to make an impact on a global scale please apply!

About the team
Work with the inventor of control barrier functions in the Safe Autonomy Frontiers (SAF) Lab. The first industry research lab in safe autonomy developing a universal safety layer for the next generation of robotic systems: mobile robots manipulators quadrupeds and humanoids. You will push the frontiers of performant safety for highly dynamic robots: CBF theory integrated with perception and learning evaluated on next-generation robots. Your work will underpin Amazons path to millions of robots operating alongside people.

- PhD in engineering technology computer science machine learning robotics operations research statistics mathematics or equivalent quantitative field
- Deep expertise in Control Barrier Functions including theoretical foundations and practical implementation
- Experience formulating and solving optimization-based controllers (QPs SOCPs) for real-time safety filtering
- 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
- Experience validating safety-critical algorithms on physical robotic hardware (not simulation-only)
- Publication record at relevant venues (e.g. CDC ACC L-CSS ICRA RSS RAL Automatica TAC TRO)

- Understanding of control systems engineering with a specific focus on layered architecture used in robotic systems (high level planning mid-level trajectory generation and low-level feedback control)
- Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches
- Experience with robust or adaptive CBF methods that account for parametric uncertainty or unmodeled dynamics
- Experience with sum-of-squares (SOS) programming Lyapunov function synthesis or other computational tools for verifying set invariance
- Familiarity with real-time embedded systems and the constraints of deploying optimization-based controllers on safety-rated hardware
- Experience applying CBFs to multi-agent systems or high-dimensional robotic platforms (manipulators legged robots)
- Exposure to learning-based approaches for CBF synthesis (e.g. neural CBFs data-driven barrier functions) and the integration of CBFs into learning (e.g. CBF-RL)

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees supervisors and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees supervisors and staff to ensure exceptional customer service; and follow all federal state and local laws and Company policies. Criminal history may have a direct adverse and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above as well as the abilities to adhere to company policies exercise sound judgment effectively manage stress and work safely and respectfully with others exhibit trustworthiness and professionalism and safeguard business operations and the Companys reputation. Pursuant to the Los Angeles County Fair Chance Ordinance we will consider for employment qualified applicants with arrest and conviction records.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including health insurance (medical dental vision prescription Basic Life & AD&D insurance and option for Supplemental life plans EAP Mental Health Support Medical Advice Line Flexible Spending Accounts Adoption and Surrogacy Reimbursement coverage) 401(k) matching paid time off and parental leave. Learn more about our benefits at CA PASADENA - 136000.00 - 184000.00 USD annually