Postdoctoral Scholar - SAF Lab, Compass

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

Benefits offered by this job

RSUs

Health insurance

401(k) match

Paid time off

Parental leave

Job summary

Amazon SAF Lab seeks a PhD‑level researcher to advance safe autonomy for mobile and robotic platforms. You will work on control barrier functions integrated with perception and learning, and evaluate methods on high‑fidelity simulators before deploying to hardware.

The role emphasizes publishing in top venues and contributing to Amazon's leadership in robotics research. You will collaborate with ML, controls, and mechanical engineering teams to set a science roadmap and translate theory into

Qualifications

• PhD in a field related to control, robotics, or learning.
• Deep understanding of safety‑critical control and safety filters.
• Proficient in C++ and Python with real control/learning code.
• Experience with physics simulators (Isaac Gym/Sim, MuJoCo, PyBullet).
• Publications in top robotics/ML venues.

Responsibilities

• Advance safe autonomy research across theory, learning, perception, and control.
• Build simulation and evaluation pipelines for large‑scale validation.
• Develop sim‑to‑real transfer pipelines for deploying controllers on hardware.
• Deploy methods on dynamically stable robots and validate in practice.
• Publish research in leading robotics, control, and ML venues.
• Collaborate with product teams and science leaders to shape a science roadmap.

Skills

C++

Python

Control algorithms

Learning policies

Safety-critical control

Publications

Education

PhD in relevant field

Tools

Isaac Gym/Sim

MuJoCo

PyBullet

Job description

Job ID: 10455421 | Amazon.com Services LLC

Job Overview

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, mobile manipulators, and future platforms with dynamic stability. 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 robots operating alongside people at Amazon's unprecedented scale.

Key Responsibilities

• Push forward the fundamental science of safe autonomy. This can be from a variety of perspectives: theoretic contributions, integration with learning, or synthesis from perception. Especially valuable are methods that bridge these different domains.
• Develop the simulation and evaluation pipelines needed to run complex and large‑scale validation of methods developed in high‑fidelity simulation environments.
• Develop sim‑to‑real transfer pipelines that enable the deployment of simulation‑based methods (controllers, policies) on hardware.
• Deploy the methods developed on hardware, with a focus on dynamically stable robots. Validate the underlying science developed in practice and identify gaps between the science and practice to drive innovation in research.
• Publish research at top‑tier robotics, control and ML venues and contribute to Amazon's scientific reputation in advanced robotics.
• Collaborate with product teams and science leaders to set a science roadmap (with eventual impact on real robots).

Basic Qualifications

• PhD in Computer Science, Robotics, Control, Mechanical Engineering, Electrical Engineering, or a related field with a focus on control, learning, and/or robotics.
• Deep understanding of safety‑critical control, including control barrier functions and safety filters.
• Proficiency in C++ and Python with experience implementing control algorithms and/or learning policies.
• Experience with physics simulators for robotics (e.g., Isaac Gym/Sim, MuJoCo, PyBullet).
• Experience validating on physical robotic hardware (not simulation‑only).
• Track record of publications at top‑tier venues in control and robotics (e.g., RSS, ICRA, IROS, CDC, CoRL, NeurIPS, ICLR, L‑CSS, RAL, TRO, TAC).

Preferred Qualifications

• Understanding of locomotion, reduced‑order models, layered control architectures, nonlinear control, reachability methods, and whole‑body control.
• Knowledge of learning‑based approaches to robotics (e.g., reinforcement learning, diffusion, VLAs, VLMs, world models).
• 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).
• 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).
• Experience with perception on robotic systems (e.g., depth camera and LiDAR‑based sensing modalities, sensor fusion, semantic tagging).
• 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).

Benefits and Compensation

The base salary range for this position is USD 136,000.00 – 184,000.00 annually. 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.

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