ML Research Engineer, Interpretable AI for End-to-End Automated Driving

Confirmed live in the last 24 hours

Toyota Research Institute

Los Altos, CA

On-site

Posted February 23, 2026

Job Description

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 advancing the state of the art in AI, robotics, driving, and material sciences.

The Team

The Automated Driving Advanced Development (AD2) division at TRI will focus on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. This new division is leading a new cross-organizational project between TRI and Woven by Toyota to conduct research and develop a fully end-to-end learned driving stack. This cross-org collaborative project is harmonious with TRI’s robotics divisions' efforts in Diffusion Policy and Large Behavior Models.

Within AD2, we are pursuing a focused research effort in Interpretable AI (iAI) for end-to-end learned automated driving systems, tightly coupled with AD2’s work on Large Behavior Models (LBM-Drive) and World Foundation Models (WFM), while remaining architecturally and product independent.

The Opportunity

We are seeking a Machine Learning Researcher to contribute to research on interpretable AI methods for learning-based automated driving systems. This role is ideal for a researcher who enjoys hands-on experimentation, model development, and evaluation, and who wants to work on foundational problems at the intersection of autonomy, interpretability, and safety. You will work closely with senior researchers and engineers to develop methods that make end-to-end neural driving policies more interpretable, diagnosable, and verifiable, while preserving performance and scalability. Your work will contribute to building “glass-box” representations that help engineers and researchers better understand, debug, and validate learned driving behaviors.

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