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

Amazon; Pasadena (CA); On-site; USD 136,000 - 184,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

RSU participation

Job summary

Amazon SAF Lab in Pasadena is seeking a Research Scientist to advance Control Barrier Functions (CBF) theory and algorithms that underpin a universal safety layer for the company\'s robotic systems. You will bridge theory and practice by deploying CBF methods on next‑generation robots and evaluating them with hardware experiments.

The role emphasizes strong expertise in CBF theory, real-time optimization, and collaboration across perception, planning, locomotion, and manipulation teams to scale

Qualifications

• PhD in engineering or related field.
• Deep expertise in Control Barrier Functions with theory and practice.
• Experience formulating and solving optimization-based controllers for real-time safety filtering.
• Strong mathematical background in dynamical systems, nonlinear control, and formal verification or reachability analysis.

Responsibilities

• Develop and implement novel CBF algorithms with formal safety guarantees while minimizing conservatism.
• Frame safety filtering within layered architectures including learning-based components.
• Design multi-layer CBF safety filters with decision making layers, MPC, and real-time nonlinear control.
• Validate algorithms through simulation and hardware experiments and publish results.

Skills

Control Barrier Functions

Real-time optimization

Robotics knowledge

Publications

Education

PhD in engineering or related quantitative field

Tools

C++

Python

Job description

We are seeking a Research Scientist to join the SAF Lab. In 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 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 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, 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,

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 Amazon's path to millions of robots operating alongside people.

Basic Qualifications

• 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)

Preferred Qualifications

• 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 Company’s 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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information.

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 https://amazon.jobs/en/benefits.

USA, CA, PASADENA - 136,000.00 - 184,000.00 USD annually