At Toyota Research Institute (TRI), we're on a mission to improve the quality of human life. We're developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we've built a world-class team in Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics. Within the Human Interactive Driving division, the Extreme Performance Intelligent Control department is working to develop scalable, human-like driving intelligence by learning from expert human drivers. This project focuses on creating a configurable, data-driven world model that serves as a foundation for intelligent, multi-agent reasoning in dynamic driving environments. By tightly integrating advances in perception, world modeling, and model-based reinforcement learning, we aim to overcome the limitations of more compartmentalized, rule-based approaches. The end goal is to enable robust, adaptable, and interpretable driving policies that generalize across tasks, sensor modalities, and public road scenarios-delivering transformative improvements for ADAS, autonomous systems, and simulation-driven software development. We are looking for a creative and rigorous Research Scientist to focus on tailoring world models for effective use in policy learning and evaluation for autonomous vehicles. In this role, you will be at the heart of research efforts that bridge perception-driven environment models and the training of intelligent decision-making policies. Your work will ensure that learned world models can serve as faithful, controllable, and informative substrates for safe and robust policy optimization and evaluation.