About the job
Our team in Amazon Robotics builds robotic systems that perform contact-rich manipulation tasks safely and reliably in complex, unstructured environments — at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to join our Motion Behaviors team. You will drive the development of learned controllers and manipulation behaviors, applying techniques like reinforcement learning and behavior cloning to robots operating in Amazon fulfillment centers. These problems remain unsolved at scale: our robots must improve continuously in environments where simulation alone is insufficient. You will make principled decisions about when learned approaches should replace engineered solutions, and how to select behaviors based on estimated risk. You will collaborate across disciplines and leverage rich operational data to continuously improve system performance.
Responsibilities
• Develop learned controllers and manipulation behaviors, from research prototyping through deployment on production robots.\n• Research, design, and implement motion planning, control, and decision-making algorithms that improve the performance of deployed systems.\n• Design and deploy learning pipelines that take policies from simulation training to reliable, real-time execution on physical robots.\n• Develop models that predict manipulation outcomes and inform behavior selection under uncertainty.\n• Leverage operational data from deployed systems to systematically identify failure modes and drive policy improvements.\n• Represent Amazon in academia through publications and scientific presentations.
Qualifications
Minimum
• PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience.\n• Experience programming in Java, C++, Python or related language.\n• Experience in patents or publications at top-tier peer-reviewed conferences or journals.\n• Experience training reinforcement learning or imitation learning policies for manipulation or robot control problems.\n• Strong background in reinforcement learning, including reward design and training at scale in simulation.
Preferred
• Experience in professional software development.\n• Experience with sim-to-real transfer (domain randomization, system identification, etc.), including transferring learned policies onto physical robots.\n• Familiarity with learned dynamics or world models for manipulation.