<div><span>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 &amp; Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.</span></div><div><br /></div><div><span>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. </span></div><div><br /></div><div><span>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.</span></div><h3>Responsibilities</h3><ul><li>Develop and refine world models that support realistic and diverse counterfactual reasoning, scenario generation, and policy rollout.</li><li>Ensure that world models are compatible with and useful for reinforcement learning, imitation learning, and offline policy evaluation techniques.</li><li>Design methods to synthesize high-risk or edge-case scenarios from world models, enabling robust stress-testing of autonomous policies.</li><li>Explore techniques such as latent-space simulation, world model distillation, differentiable simulation, and closed-loop evaluation to improve policy development and evaluation pipelines.</li><li>Partner with researchers in world modeling, planning, and safety evaluation to co-develop aligned architectures and learning objectives to ensure that learned models accurately capture agent-environment dynamics relevant to long-horizon planning and safety-critical decision-making.</li><li>Publish high-quality research and contribute to the community through open-source tools, benchmarks, and conference participation.</li></ul><h3>Qualifications</h3><ul><li>PhD in Computer Science, Robotics, Machine Learning, or a related field.</li><li>Strong background in at least two of the following areas: World models or model-based reasoning in dynamic environments, World model adaptation and fine-tuning, Offline RL or imitation learning, Model-based reinforcement learning (MBRL), Simulation-to-reality transfer, or Policy evaluation and safety assurance.</li><li>A track record of high-quality publications in ML or robotics venues (e.g., ICML, ICLR, NeurIPS, CoRL, RSS).</li><li>Familiarity with latent dynamics models (e.g., Dreamer, PlaNet, MuZero).</li><li>Understanding of uncertainty modeling, generalization, and robustness in learned <a href="http://environments.Experience">environments.Experience</a> evaluating autonomous vehicle policies in simulation and real-world settings.</li><li>Experience in building or applying models for downstream evaluation of autonomous systems.</li><li>Proficiency in Python and ML frameworks (e.g., PyTorch, JAX).</li></ul><div><b>Please submit a brief cover letter and add a link to Google Scholar to include a full list of publications when submitting your CV for this position.</b></div><div><br /></div><div><i>The pay range for this position at commencement of employment is expected to be between $176,000 and $264,000/year for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave) and an annual cash bonus structure. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.</i></div><div><br /></div><div><span>Please reference this </span><a href="https://www.tri.global/ccpa-privacy-policy/"><span>Candidate Privacy Notice</span></a><span> to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.</span></div><div><br /></div><div><span>TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.</span></div><div><br /></div><div><span>It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment. </span></div>