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

## 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. ## Requirements - 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). ## Nice to Have - 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 ## Benefits 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.