We are looking for a robotics scientist to build and operate the first autonomous materials discovery laboratory at Amazon. This role combines deep robotics expertise (motion planning, control, platform integration) with modern Physical AI approaches (vision-language-action models, sim-to-real transfer, agentic orchestration). You will design autonomous experimental workflows that integrate dexterous robotic platforms, analytical instruments, and AI-driven hypothesis generation into a closed-loop discovery pipeline - where foundation models drive hypothesis generation and experimental planning, validated on real hardware under real chemistry. This is not a pure research role. You will work directly with physical robots, laboratory instruments, and deployment pipelines. The work is expected to be published, but the primary measure of success is a working autonomous platform that generates scientific results. Materials science expertise is not required - the team includes domain scientists. What matters is strong AI and robotics foundations, scientific curiosity, and the drive to ship. 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. - Architect 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. 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. 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, , 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). 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. USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually