Automated Driving Advanced Development Intern, Machine Learning Research at Toyota Research Institute

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!

Responsibilities

• Conduct research to advance the state of the art in sim-to-sim and sim-to-real domain mapping, enabling driving policies post-trained in fast, low-fidelity simulation to transfer to higher-fidelity simulation and real-world driving.
• Design and run experiments using large-scale Toyota driving data, the team’s generative base models, and closed-loop simulation infrastructure.
• Perform closed-loop evaluations across simulators of varying fidelity, and develop metrics that measure how well post-trained behaviors carry over between domains.
• Collaborate with researchers and engineers to integrate the tools and findings from your project into the team’s model development and post-training workflows.
• Present progress and results regularly to the team, and incorporate feedback into the research direction.
• Write up your findings as a paper for submission to a conference or workshop by the end of the internship.

Qualifications

• Currently pursuing a Ph.D. or master’s degree in Computer Science, Robotics, Machine Learning, Artificial Intelligence, Engineering, or a related field.
• Proficiency in Python and ML frameworks such as PyTorch for implementing and evaluating research ideas, along with an understanding of version control, testing, and software engineering fundamentals.
• Research experience in one or more of the following: sim-to-real transfer, domain adaptation, reinforcement learning, imitation learning, generative models such as diffusion or flow matching, or autonomous driving.
• Demonstrated research ability, such as publications, preprints, or substantial independent research projects.
• Ability to work independently on an open-ended research problem, and to communicate methods and results clearly.
• Able to work in a hybrid in-office arrangement at TRI’s headquarters in Los Altos, California, for the full 12-week internship.

Bonus Qualifications

• Experience with driving or robotics simulators such as CARLA or NVIDIA DriveSim, or with sensor simulation and neural rendering.
• Experience with world models or generative models for video or scene reconstruction.
• Experience post-training policies with reinforcement learning, DAgger, or preference-based methods.
• Hands-on experience working with real-world sensor data, such as video, lidar, IMU, or radar.
• Experience with large-scale distributed training and experiment tracking tools such as Weights & Biases.
• Publications at venues such as CoRL, ICRA, RSS, NeurIPS, ICML, or CVPR.