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

Source: https://realanalystjobs.com/job/applied-and-research-scientist-safe-autonomy-frontiers-saf-lab-safe-autonomy-fro-112ad239
Applied & Research Scientist - Safe Autonomy Frontiers (SAF) Lab, Safe Autonomy Frontiers (SAF) Lab at Amazon, an on-site role based in Pasadena, California, USA. Experience level: 3+ Years. Posted on 21 October 2026, read from Amazon's own careers page (Amazon), which RealAnalystJobs re-checks daily and removes the role when it closes. Amazon has approved H-1B petitions on USCIS record (employer-wide, not a promise for this role).

Updated 15 November 2026.

## Key facts

- Company: [Amazon](https://realanalystjobs.com/companies/amazon)
- Location: Pasadena, California, USA
- Work mode: On-site
- Posted: Oct 21, 2026
- Employment type: Full time
- Experience level: 3+ Years
- Source: Amazon's careers page (Amazon), re-checked daily

Skills: [Python](https://realanalystjobs.com/skills/python)

## Visa sponsorship

The posting does not say.

Amazon had H-1B petitions approved for 12,010 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/amazon#h1b-record)

## About this role

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 equivalent 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

Description from Amazon's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you.

## How to apply

Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/applied-and-research-scientist-safe-autonomy-frontiers-saf-lab-safe-autonomy-fro-112ad239) and use Apply now there to reach the employer's application. The listing is re-checked against Amazon's careers page daily and removed when it closes.

## Amazon

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