Human Interactive Driving Research Intern, World Models 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!

The Team

You will be joining the Extreme Performance Intelligent Control (EPIC) team in the Human Interactive Driving division, a multidisciplinary research team focused on developing a world foundation model for driving—a unified, transferable representation of driving knowledge built from large-scale real-world and simulated data. Together, we are tackling the complex challenges of multi-agent interaction, causal reasoning, and embodied intelligence in highly dynamic, real-world driving environments. Our approach is inspired by cutting-edge generative modeling techniques and powered by scalable infrastructure and open-source data, and we work closely with engineers to deploy our models in simulation and on hardware.

The Internship

As a Ph.D. Research Intern, you will conduct original research at the intersection of machine learning and autonomous driving. Your work will focus on advancing the state of the art in world modeling across various areas, including representation learning, action conditioning, and evaluation. You will have the opportunity to prototype and evaluate your ideas in closed-loop simulations and contribute to research publications alongside TRI scientists. This is an opportunity to test and refine your research in a collaborative, high-impact environment, while working with real-world data and contributing to the future of autonomous systems.

Responsibilities

Conduct original research in one or more areas: world modeling, multi-agent interaction, reinforcement learning, perception, or sim-to-real transfer.

Collaborate closely with full-time researchers on the design, training, and evaluation of learning-based driving systems.

Develop and evaluate prototypes in closed-loop simulation environments and, time permitting, on high-performance autonomous driving hardware.

Present research findings through internal talks and work towards a top-tier academic publication.

Integrate and work with large-scale datasets (open-source and internal).

Qualifications

Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related fields.

Strong background in machine learning, particularly in areas such as deep learning, generative models, reinforcement learning, or probabilistic modeling.

Hands-on experience training and evaluating world models, model-based reinforcement learning, 3D reconstruction or sensor fusion, and large-scale simulation for robotics or autonomous systems.

Proficient coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow, etc.).

Prior publication(s) in top-tier AI/ML conferences.

Bonus Qualifications

Probabilistic and generative modeling.

Autonomous driving datasets.

Open-source foundation models.

Amazon Web Services (e.g., S3, EC2, SageMaker, etc.).

Please include links to any relevant open-source contributions or technical project write-ups with your application.

The pay range for this position at commencement of employment is expected to be between $45 and $65/hour for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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