Are you interested in how to build Artificial General Intelligence (AGI)? Are you excited by science at the interface of classical AI reasoning and Large Language Models (LLMs)? Would you like to apply your technology to serve customers better? Amazon is looking for talented Postdoctoral Scientists to join our Amazon AGI (Artificial General Intelligence) in Fulfillment team for a one-year, full-time research position. Postdoctoral Scientists will innovate on the fundamental science of AGI, including working on combining LLMs with more classical AI task planning and AI reasoning. You will work with a team of scientists and engineers to achieve this. You will publish your results in two papers at leading venues in AI. You will be part of a larger team and have the opportunity to work on problems such as: using LLMs to generate robot task plans, using AI reasoning to verify whether a plan is correct, learning state space models and learning efficient reasoning strategies. You will have access to our latest LLMs and the opportunity to work on problems that can be applied to robotics, automation and fulfillment. Key job responsibilities In this role you will: • Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. • Publish your innovation in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. A day in the life You'll meet with your science advisor on your ideas for the project, get guidance and feedback, work together on architectures and algorithms to achieve your goals, author papers, and understand better how science works at Amazon. You'll work closely with other scientists to review your plans and results. You'll meet with engineers to implement the architecture, carry out training and inference. About the team The Amazon AGI for Fulfilment Team is a new science team working at the boundary between LLMs, classical AI planning and reasoning, inspired by our problems in fulfillment, automation and robotics. We focus on high quality research science informed by practical problems.