Automated Driving Advanced Development Intern, Machine Learning Research

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.

This is a Summer 2026 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

The Team

The Automated Driving Advanced Development 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.

The Internship

We are looking for Machine Learning Research Interns to join our autonomy team and help bring end-to-end ML models ( pixels to trajectories ) into robust, testable, and deployable systems. This role is ideal for those who thrive at the intersection of machine learning, systems engineering, and real-world deployment. This internship opportunity is a paid 12-week internship for Summer 2026. Please note that this internship will be a hybrid in-office role.

You'll contribute to the implementation, evaluation, and integration of ML-based components for perception, planning, and control; with simulation-based testing. You'll work closely with researchers, data engineers, and autonomy engineers to ensure models scale from prototype to production. This work is part of Toyota's global AI efforts to build a more coordinated global approach across Toyota entities.

Responsibilities

• Conduct ambitious research to advance the state-of-the-art in using new capabilities in generative modelling for end-to-end planning from vision in automated driving.
• Implement scalable end-to-end architectures that process raw sensor data to generate vehicle trajectories, addressing the challenges of long-tail driving scenarios with low data coverage.
• Prototype, validate, and iterate on model architectures using imitation learning, and large-scale data, ensuring robust performance across diverse scenarios.
• Perform closed-loop evaluations in sensor simulations and real-world testing environments.
• Explore multi-modal and language-conditioned models to broaden the applicability of end-to-end policies, leveraging external data sources and