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 paid 12-week internship opportunity and is a hybrid, in-office role.

Here’s a glimpse into the Internship experience from some of our TRI interns!

The Team

The Automated Driving Advanced Development division bridges TRI research and Toyota products. In partnership with Woven by Toyota, the team is researching and developing an end-to-end learned driving stack that maps sensor inputs directly to planned trajectories.

The Internship

We’re seeking a research intern to investigate how driving policies post-trained in fast, low-fidelity simulation can transfer to higher-fidelity simulation and real-world driving. This is an open research problem: differences in sensor inputs, vehicle dynamics, and other agents can limit transfer, and policies may learn simulator-specific behavior or forget previously learned skills.

You’ll lead an independent research project exploring approaches to sim-to-sim and sim-to-real transfer for sensor-based driving models. You’ll work with Toyota driving data, generative base models, and closed-loop simulation infrastructure, with the goal of producing work suitable for a conference or workshop submission.

The Opportunity

As a research intern on AD2, you will own a standalone research project in sim-to-sim and sim-to-real domain mapping, with the opportunity to:

Drive your own research: Shape the approach, run experiments, and analyze results, with the independence to set your own direction.

Publish your findings: The project is designed to produce results suitable for a conference or workshop publication.

Shape a real autonomy model: The tools and insights you create will help the team post-train our autonomy model more effectively, with the potential to influence the autonomy stack eventually deployed in Toyota vehicles.

Collaborate with experienced researchers: Work closely with a small team of industry-leading researchers with deep experience in robotics and autonomous driving.

Work at scale: Use the same large-scale Toyota driving data, base models, and simulation infrastructure the team uses every day.

You can learn more about our work on the Automated Driving Advanced Development team page .