Human Interactive Driving Research Intern, World Models

Toyota Research Institute•Los Altos, CA

5d•Hybrid

About The Position

Toyota Research Institute is hiring a paid 12-week Ph.D. Research Intern for its Extreme Performance Intelligent Control (EPIC) team in the Human Interactive Driving division. The team is developing a world foundation model for driving: a unified, transferable representation of driving knowledge based on large-scale real-world and simulated data. Its work addresses multi-agent interaction, causal reasoning, and embodied intelligence in dynamic driving environments, using generative modeling, scalable infrastructure, and open-source data. The team collaborates with engineers to deploy models in simulation and on hardware. The intern will conduct original research at the intersection of machine learning and autonomous driving, advancing world modeling in areas such as representation learning, action conditioning, and evaluation. The work includes prototyping and evaluating ideas in closed-loop simulation, working with real-world and large-scale datasets, and contributing to research publications alongside TRI scientists. This is a hybrid, in-office internship. California-based pay is expected to be $45–$65 per hour at commencement; actual base pay depends on individualized factors including experience, skills, job-related knowledge, and market location.

Requirements

Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field.
Strong background in machine learning, particularly 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 and expertise with modern deep learning frameworks such as PyTorch, JAX, or TensorFlow.
Prior publication(s) in top-tier AI/ML conferences.

Nice To Haves

Experience with probabilistic and generative modeling.
Experience with autonomous driving datasets.
Experience with open-source foundation models.
Experience with Amazon Web Services, such as S3, EC2, or SageMaker.
Relevant open-source contributions or technical project write-ups (links requested with the application).

Responsibilities

Conduct original research in one or more areas: world modeling, multi-agent interaction, reinforcement learning, perception, or sim-to-real transfer.
Collaborate 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 toward a top-tier academic publication.
Integrate and work with large-scale open-source and internal datasets.