Applied Scientist 2

Description

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 job 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.

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, please apply!

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.
The team delivers end-to-end simulation stacks for Amazon's robotics programs, including high-fidelity robot digital twins, teleoperation data collection infrastructure, scalable synthetic demonstration generation, policy training and inference pipelines (RL, imitation learning, VLAs), domain randomization for sim-to-real transfer, and model validation in simulation. We partner closely with hardware teams, science organizations, and robotics program leads across Amazon Robotics.

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.

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

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.