Applied Scientist III — Robotics & Physical AI, Autonomous Lab, WW Sustainability

Description

Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices.

About the team

Sustainability Science and Innovation (SSI) is a multi-disciplinary research team within WW Sustainability combining science, ML, economics, and engineering. The autonomous laboratory is a new capability being built from the ground up. You will work alongside computational materials scientists, chemists, and ML engineers — with access to AWS-scale compute and Amazon's supply chain for hardware. The work targets sustainability outcomes across packaging, building materials, and alternative fuels.

Key job responsibilities

• Develop, train, and benchmark robotic manipulation policies for materials synthesis and characterization using modern policy architectures (VLA architectures, diffusion policies).
• Design and execute sim-to-real transfer strategies including domain randomization, physics parameter tuning, and visual domain adaptation for laboratory robotic systems.
• Integrate robotic platforms and laboratory instruments into automated workflows via APIs (SiLA 2, or equivalent), building real-time data pipelines for multimodal experimental outputs.
• Arcitect policy training pipelines combining teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning for dexterous lab manipulation.
• Build production-grade agentic runtime systems — failure detection, retry logic, exception handling, and human-handoff protocols — for unattended experimental sessions.
• Design and execute autonomous experimental campaigns applying active learning, Bayesian optimization, or RL to drive iterative materials discovery.
• Drive technical design reviews and set scientific direction for the autonomous lab platform.

A day in the life

You build the Physical AI systems that power robotics in autonomous science lab, one where foundation models generate hypotheses, robots execute experiments, and closed-loop optimization discovers materials that did not exist yesterday. You train manipulation policies in simulation, transfer them to a physical cobot, and watch real chemistry validate (or invalidate) an AI-generated theory. The signal here is not a metric on a dashboard; it is a synthesizing and testing novel material with measurable sustainability impact. If you want your research to have physical weight, this is the lab.

Basic Qualifications

• Master's degree, or PhD
• 3+ years of industry or academic research experience
• Knowledge of programming languages such as C/C++, Python, Java or Perl
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow.

Preferred Qualifications

• First-hand sim-to-real transfer experience: policies trained in simulation, successfully deployed on physical hardware.
• Experience with VLA or robot policy architectures (OpenVLA, π0, RT-2, or equivalent).
• 2+ years with collaborative robot platforms including motion planning, impedance/force control, and multi-step manipulation.
• Experience building agentic AI systems for multi-step workflows including failure recovery and foundation model reasoning.
• Experience with self-driving laboratory (SDL) systems or automated chemical synthesis platforms.
• Publications in top-tier venues (NeurIPS, ICML, ICLR, ICRA, CoRL, RSS).