Applied Scientist II - Robotics Simulation, Amazon Robotics R&D

Amazon; Boston (MA); On-site; USD 142,800 - 193,200; Full time

Benefits offered by this job

RSUs

Sign-on bonus

Job summary

Amazon Robotics is hiring an Applied Scientist II for Robotics Simulation in Boston. You will design GPU-accelerated RL environments, build policy training pipelines, and validate simulations against real hardware.

You will drive sim-to-real transfer research, implement domain randomization, and validate end-effector interactions to accelerate robotics development. You will lead end-to-end simulation deliverables, collaborate with SDEs, Technical Artists, and partner science teams, and

Qualifications

• PhD or Master’s degree in a relevant field.
• Proficiency with ML frameworks (JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, TensorRT).
• 2+ years with physics simulation platforms for robot learning (MuJoCo, Isaac Sim/Lab, PyBullet, Drake).
• Experience training robot policies with RL or imitation learning and evaluating in simulation.
• Experience with articulated robot simulation (URDF/MJCF/USD).
• Familiarity with sim-to-real transfer concepts (domain randomization, system identification, physics calibration).

Responsibilities

• Design and implement GPU-accelerated RL and imitation learning environments for manipulation and mobility tasks.
• Build and maintain policy training pipelines for diverse model architectures and evaluate in simulation.
• Characterize and reduce sim-to-real gaps using simulated vs real robot data, implement improvements.
• Apply domain randomization to improve policy robustness and transfer to real hardware.
• Develop sim-to-real transfer techniques including system identification and physics calibration.
• Create robot embodiment validation tests to ensure fidelity to hardware.
• Build data pipelines for recording, replaying, and augmenting demonstration data.
• Contribute to end-effector modeling and contact dynamics tuning in simulation.
• Author design documents and participate in technical reviews.
• Collaborate with partner science teams to align with training requirements.

Skills

ML frameworks: JAX, PyTorch, TensorRT

Reinforcement learning

Imitation learning

Robotics simulation

URDF/MJCF/USD familiarity

Sim-to-real transfer

Education

PhD or Master’s degree

Tools

MuJoCo

Isaac Sim/Lab

PyBullet

Drake

Job description

Applied Scientist II - Robotics Simulation, Amazon Robotics R&D

Job ID: 10470562 | Amazon.com Services LLC

We are looking for an Applied Scientist to join the Robotics Simulation team at Amazon Robotics. In this role you will design, build, and validate the simulation environments and policy training pipelines that enable robots to learn manipulation and mobility skills in simulation and transfer them to real hardware.

You will work at the intersection of robotics simulation science and modern Physical AI: building GPU-accelerated RL environments, implementing imitation learning workflows, characterizing sim-to-real gaps, tuning physics parameters against real-world data, and evaluating learned policies both in simulation and on physical robots. You will collaborate closely with SDEs who build platform infrastructure, Technical Artists who create simulation assets, and partner science teams who consume your environments and pipelines for their model development.

This is a hands‑on, execution‑focused role. You will own specific simulation science deliverables end‑to‑end, from environment design through policy evaluation, with increasing scope and independence over time. You will contribute to technical design discussions, propose improvements to the team’s simulation fidelity and training methodology, and help establish best practices for robot learning in simulation.

Key Responsibilities

• Design and implement GPU-accelerated reinforcement learning and imitation learning environments in NVIDIA Isaac Lab for manipulation and mobility tasks.
• Build and maintain policy training pipelines supporting diverse model architectures (diffusion policies, VLAs, behavior cloning, actor-critic RL) and evaluate trained policies in simulation.
• Characterize and reduce sim-to-real gaps through systematic validation: compare simulated sensor outputs, kinematics, and dynamics against real-world robot data, then implement targeted improvements.
• Implement domain randomization strategies (visual, physics, geometric) to improve policy robustness and transfer to real hardware.
• Develop sim-to-real transfer techniques including system identification, physics parameter calibration, and visual domain adaptation.
• Create robot embodiment validation tests (joint kinematics, actuator response, contact behavior) to ensure digital twins are faithful to real hardware.
• Build data pipelines for recording, replaying, and augmenting demonstration data (from teleoperation or automated trajectory generation) to scale training data volume.
• Contribute to end-effector modeling and contact dynamics tuning, ensuring physically plausible gripper and tool interactions in simulation.
• Author design documents for new simulation science capabilities and contribute to technical reviews.
• Collaborate with partner science teams to understand their model architectures and ensure simulation environments meet their training requirements.

Benefits

Amazon offers a full range of benefits that support you and your eligible family members.

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

Additional benefits vary by location, the number of regularly scheduled hours, length of employment, and job status such as seasonal or temporary employment. Full details can be found on Amazon’s benefits website.

About the team

The Robotics Simulation team is a multidisciplinary organization of SDEs, Applied Scientists, and Technical Artists at Amazon Robotics. We build the simulation infrastructure that powers Physical AI development, from photorealistic synthetic data to GPU-accelerated training environments. Our simulation stack enables robots to be designed, trained, and validated entirely in simulation before physical hardware exists, compressing development timelines and de-ris­king robotics programs across Amazon.

Basic Qualifications

• PhD, or Master’s degree
• Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
• Experience in robotics design, automation systems development, control systems design, or related product development
• 2+ years of experience working with physics simulation platforms for robot learning (MuJoCo, Isaac Sim/Lab, PyBullet, Drake, or equivalent)
• Demonstrated experience training robot policies using reinforcement learning or imitation learning and evaluating them in simulation
• Experience with articulated robot simulation, including URDF/MJCF/USD formats and rigid/soft body dynamics
• Familiarity with sim-to-real transfer concepts (domain randomization, system identification, or physics calibration)

Preferred Qualifications

• Hands‑on experience deploying learned policies on real robot hardware (manipulation arms, mobile platforms, or mobile manipulators)
• Experience with NVIDIA Isaac Lab/Sim, Omniverse, or USD-based simulation workflows
• Experience with modern Physical AI architectures: vision-language-action models, diffusion-based policy learning, action-chunking transformers, or behavior cloning from demonstrations
• Familiarity with teleoperation systems and demonstration data collection pipelines (haptic devices, recording in HDF5/zarr, or similar)
• Experience with contact dynamics modeling and parameter tuning for grippers, suction systems, or other end-effectors
• Familiarity with ROS2 and robotics middleware for integration with robot software stacks
• Experience with GPU-accelerated parallel simulation (running thousands of environments concurrently for RL training)
• Experience with trajectory optimization or motion planning (MoveIt, OMPL, or equivalent) in the context of generating training data
• Publications at robotics or ML venues (CoRL, ICRA, IROS, RSS, NeurIPS, ICML) are a plus but not required

The base salary range for this position is 142,800.00 – 193,200.00 USD 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, 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.