Robotics Intern Large Behavior Models, Policy Training

Los Altos, CA - USA

Hourly Salary: $ 45 - 65

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

At Toyota Research Institute (TRI) were on a mission to improve the quality of human life. Were developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility weve built a world-class team in Automated Driving Energy & Materials Human-Centered AI Human Interactive Driving Large Behavior Models and Robotics.

This is a summer 2026 paid 12-week internship opportunity. Please note that this internship will be an in-office role.

The Mission

We are working to create general-purpose robots capable of accomplishing a wide variety of dexterous tasks. To do this our team is building general-purpose machine learning foundation models for dexterous robot manipulation. These models use generative AI techniques to produce robot action from sensor data and human request. To accomplish this we are creating a large curriculum of embodied robot demonstration data and combining that data with a rich corpus of internet-scale text image and video data. We are also using high-quality simulation to augment real world robot data with procedurally-generated synthetic data.

The Team

The Robotics Machine Learning Teams charter is to push the frontiers of research in robotics and machine learning to develop the future capabilities required for general-purpose robots able to operate in unstructured environments such as homes or factories.

The Internship

We have several research thrusts under our broad mission and we are looking for a research intern in any of these areas:

Data-efficient and general algorithms for learning robust policies leveraging multiple sensing modalities: proprioception images 3D representations etc.

Data annotation and filtering. Improving policies without collecting more data by using non-robotics data at scale new training objectives new data annotations or filtered datasets.

Scaling learning approaches to large-scale models trained on diverse sources of data including web-scale text images and video.

Structured hierarchical reasoning using learned models

Leveraging test time compute for embodied applications

Multimodal reasoning models

Reinforcement Learning for multimodal models

Leveraging history and memory for learning policies for long context tasks.

Improving robustness and few-shot generalization by leveraging sub-optimal and self-play data.

Interactive agents that can reduce the embodied and instructional ambiguity and can seek help and clarification.

The intern who joins our team will be expected to create working code prototypes interact frequently with team members run experiments with both simulated and real (physical) robots and participate in publishing the work to peer-reviewed venues. Were looking for an intern who is comfortable working with both existing large static datasets as well as a growing dynamic corpus of robot data.

Qualifications

Experience with machine learning and familiarity with large multimodal datasets and models. OR Hands-on experience with using machine learning for learned control including RL offline RL or behavior cloning for manipulation.
Strong software development skills in Python.
A make it happen attitude and comfort with fast prototyping.
A passion for robotics and doing research grounded in important fundamental problems.

Bonus Qualifications

Hardware experience.

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 business or organizational needs market location job-related knowledge skills and experience. 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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Required Experience:

Intern