Sr. Applied & Research Scientist - Safe Autonomy Frontiers (SAF) Lab, Safe Autonomy Frontiers (SAF) Lab

AI in this role

Develop and deploy safe autonomy, control barrier functions, and reinforcement learning for dynamic robots.

roboticsreinforcement-learningcontrol-theoryautonomous-systemsmachine-learning

We are seeking Applied and Research Scientists to join the Safe Autonomy Frontiers (SAF) Lab. In this role, you will develop and deploy the science of safe autonomy on highly dynamic robots, advancing the frontier through both foundational research and realization on hardware. You will contribute to achieving safe autonomy across three key areas: control barrier function (CBF) theory for robust and performant safety on hardware; safe reinforcement learning for agile whole-body control; and layered safety filters that interface with learning, perception, and semantic reasoning systems. You will validate your work experimentally on state-of-the-art robotic platforms — removing bottlenecks to deployment and enabling robots to safely operate around humans. You will work with the inventor of CBFs, as well as top scientists and engineers at Amazon developing the next generation of safe autonomy.

Key job responsibilities
-Advance the science of safe autonomy from formal foundations to the integration with learning and perception to validation on hardware — with particular emphasis on methods that bridge these domains.
-Test and validate hardware, with a focus on next generation robotic systems. Including locomotion, manipulation and loco-manipulation.
-Leverage the deployments on hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research. hardware to validate the underlying science, identifying gaps between theory and practice that drive the next cycle of research.
-Publish research at top-tier robotics, control, and ML venues, and contribute to Amazon’s scientific reputation in advanced robotics.
-Collaborate with SAF Lab and Amazon production teams to move research into robots deployed at Amazon scale.

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:
-Medical, Dental, and Vision Coverage
-Maternity and Parental Leave Options
-Paid Time Off (PTO)
-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
The Safe Autonomy Frontiers (SAF) Lab is the first industry research lab dedicated to safe autonomy, founded by the inventor of control barrier functions. We are developing a universal safety layer for the next generation of robotic systems: mobile robots, manipulators, quadrupeds, and humanoids. Here you will advance performant safety for highly dynamic robots — CBF theory integrated with perception and learning, evaluated on next-generation platforms — and your work will underpin robots operating alongside people at Amazon’s unprecedented scale.

Basic qualifications

- PhD, or Master's degree and 3+ years of industry or academic research experience
- Master’s degree and 3+ years of applied science, research, or robotics engineering experience — OR a PhD — in computer science, robotics, control, mechanical engineering, electrical engineering, or a related field.
- Hands-on experience developing and deploying control algorithms and/or learning policies on physical robotic hardware in a research or production setting (not simulation-only).
- Working knowledge of safety-critical control, including control barrier functions and safety filters.
- Proficiency in C++ and Python, with a track record of implementing control algorithms and/or learning policies in real systems.
- Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet).
- Demonstrated technical impact through shipped systems, patents, or publications at top-tier venues (e.g., CDC, ACC, L-CSS, ICRA, IROS, RSS, RA-L, Automatica, TAC, TRO).

Preferred qualifications

- Experience in professional software development, including production code quality, testing, and deployment practices.
- Experience taking robotics or autonomy research from prototype to deployed product, including collaboration with hardware, product, and operations teams.
- Understanding of autonomous systems, reduced-order models, layered control architectures, nonlinear control, reachability methods, legged locomotion, and whole-body control.
- Knowledge of learning-based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models).
- Exposure 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).
- Understanding of control systems engineering, with a focus on layered architecture in robotic systems (high-level planning, mid-level trajectory generation, low-level feedback control).
- Experience with perception on robotic systems (e.g., depth-camera and LiDAR-based sensing, sensor fusion, semantic tagging), together with mapping and navigation.
- Familiarity with Hamilton-Jacobi reachability analysis and its relationship to CBF-based approaches.
- Knowledge of safety-constrained RL (e.g., constrained MDPs, Lagrangian methods, shielding, CBF-based policy filtering).
- Experience with model-based control (MPC, whole-body QP controllers, operational space control) and/or simulation-based predictive control (MPPI).
- Experience with hierarchical RL, skill composition, distillation, and multi-task policy architectures for locomotion.
- Familiarity with real-time deployment constraints (latency budgets, onboard compute limitations, control-loop frequencies).
- Experience building or contributing to large-scale RL training infrastructure (distributed training, GPU clusters).
- Strong communication skills and ability to work across disciplinary boundaries (ML, controls, mechanical engineering).

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. If the country/region you’re applying in isn’t 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 https://amazon.jobs/en/benefits.

USA, CA, PASADENA - 167,100.00 - 226,100.00 USD annually

How we rate this

Sr. Applied & Research Scientist - Safe Autonomy Frontiers (SAF) Lab, Safe Autonomy Frontiers (SAF) Lab at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Sr. Applied & Research Scientist - Safe Autonomy Frontiers (SAF) Lab, Safe Autonomy Frontiers (SAF) Lab is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $167k-$226k a year.
The role is based in US, CA, Pasadena.
The job was posted on October 5, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RoboticsReinforcement LearningControl TheoryAutonomous SystemsMachine Learning

Questions you could be asked

Tell me about a project where robotics was part of your work. What did you do?
Tell me about a project where reinforcement learning was part of your work. What did you do?
Tell me about a project where control theory was part of your work. What did you do?
Tell me about a project where autonomous systems was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Robotics, Reinforcement Learning, Control Theory, Autonomous Systems, and Machine Learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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