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

About the role

The Robotics Simulation team at Amazon Robotics is seeking a Senior Applied Scientist to join the Robotics Simulation team. This role combines deep traditional robotics expertise with modern Physical AI approaches, bridging the gap between simulation and real robots.

Responsibilities

• Provide technical robotics direction for the team's Physical AI program, spanning simulation environment design, policy training, sim-to-real transfer, and real-world validation across multiple robotics platforms.
• Mentor junior applied scientists and engineers on robot learning best practices, helping them diagnose sim-to-real gaps, debug policy failures on hardware, and iterate toward deployable solutions.
• Design and execute sim-to-real transfer strategies, including system identification, domain randomization, physics parameter tuning, and visual domain adaptation, drawing on both classical and learned approaches.
• Arcitect policy training pipelines that combine teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning (e.g., VLA models, diffusion policies, behavior cloning) for manipulation tasks.
• Lead sim-to-real analysis: define metrics and methodologies for evaluating simulation fidelity, identifying where simulation diverges from reality, and prioritizing modeling improvements that impact downstream policy performance.
• Collaborate with hardware teams on robot embodiment modeling, ensuring that digital twins accurately capture kinematics, joint dynamics, actuator limits, contact behavior, and sensor characteristics.
• Evaluate and integrate state-of-the-art approaches from the Physical AI research community (foundation models for robotics, world models, action-chunking transformers, generalist policies) into the team's simulation and training infrastructure.
• Contribute to end-effector modeling and physics tuning, ensuring physically plausible contact interactions and accurate tool behavior in simulation across diverse manipulation hardware.
• Drive technical design reviews, author high-level design documents, and set the scientific direction for simulation fidelity and robot learning initiatives.

Qualifications

• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning
• Broad experience across a range of physics simulators (IsaacSim, IssacLab, MuJoCo, Drake, etc.)
• First-hand experience in sim2real transfer (i.e. developing learned policies in sim and successfully getting them to work on real robots)
• Deep expertise in robotics (controls, motion planning, perception, etc.), ideally both in the context of manipulation and locomotion
• Deep expertise in reinforcement learning, especially in the context of robotics
• Experience with VLAs and using simulation for data generation and benchmarking
• Experience with ROS2