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!

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.