Robotics Simulation Scientist — GPU RL & Sim-to-Real

Amazon; Sunnyvale (CA); On-site; USD 143,000 - 193,000; Full time

Job summary

Amazon Robotics is seeking an Applied Scientist II to design, build, and validate simulation environments and policy training pipelines enabling robots to learn manipulation and mobility skills in simulation and transfer to real hardware.

You will own simulation deliverables end-to-end, contribute to design discussions, and help establish best practices for robot learning in simulation.

Qualifications

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

Responsibilities

• Design and implement GPU-accelerated RL and imitation learning environments in NVIDIA Isaac Lab for manipulation and mobility tasks.
• Build and maintain policy training pipelines supporting diverse model architectures and evaluate trained policies in simulation.
• Characterize and reduce sim-to-real gaps through validation against real-world data and implement improvements.
• Implement domain randomization strategies 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 to ensure digital twins match real hardware.
• Build data pipelines for recording, replaying, and augmenting demonstration data to scale training data volume.
• Contribute to end-effector modeling and contact dynamics tuning for realistic interactions.
• Author design documents for new simulation science capabilities and participate in technical reviews.
• Collaborate with partner science teams to align environments with training requirements.

Skills

ML frameworks

Robotics design

Physics simulation platforms

Reinforcement/imitation learning

Articulated robot simulation

Sim-to-real transfer concepts

Education

PhD or Master’s degree

Job description

Amazon Robotics is seeking an Applied Scientist II to design, build, and validate simulation environments and policy training pipelines enabling robots to learn manipulation and mobility skills in simulation and transfer to real hardware.

You will own simulation deliverables end-to-end, contribute to design discussions, and help establish best practices for robot learning in simulation.