Robotics Intern - Large Behavior Models, Trustworthy Learning under Uncertainty (TLU) at Toyota Research Institute

Los Altos, California, United States

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Not SpecifiedCompensation

InternshipExperience Level

InternshipJob Type

UnknownVisa

Robotics, Automotive, Artificial IntelligenceIndustries

Requirements

Currently pursuing a Ph.D. in Machine Learning, Robotics, or related fields
Publication or desire to publish at high-impact conferences/journals (e.g., CoRL, ICLR, NeurIPS, ICML, UAI, AISTATS, AAAI, TMLR, RSS, ICRA, IROS, RA-L, T-RO, CDC, L4DC, etc.) on topics such as Policy Evaluation, Failure Detection and Mitigation, Active Learning, Multi-Modal Foundation Models, Generative Modeling, Imitation Learning, Reinforcement Learning, Planning & Control, Statistics, Uncertainty Estimation, Out-of-Distribution Detection, Safety-Aware & Robust ML, (Inter)Active Learning, and Online / Continual Learning
Passionate about large scale challenges in ML grounded in physical systems, especially in the space of robotics
Proficiency with one or more coding languages and systems, preferably Python, Unix, and a Deep Learning framework (e.g., PyTorch)
Ability to collaborate with other researchers and engineers of the TLU team, and, more broadly, the Robotics division to invent and develop interesting research ideas
A reliable teammate who loves to think big, go deeper, and deliver with integrity
Provide a link to Google Scholar and include a full list of publications when submitting CV

Responsibilities

Conduct daring research primarily in Robotics that solves open problems of high practical and/or ethical value and validate it in real-world benchmarks and systems
Push the boundaries of knowledge and the state-of-the-art in Robotics and LBMs
Partner with a multidisciplinary team, including other research scientists and engineers across the TLU team, Robotics division, TRI, Toyota, and university partners
Stay up to date on the state-of-the-art in Machine Learning ideas and software
Present results in verbal and written communications at international conferences, internally, and via open-source contributions to the community

Skills

Key technologies and capabilities for this role

Machine LearningRoboticsReinforcement LearningImitation LearningGenerative ModelingUncertainty EstimationActive LearningPolicy EvaluationOut-of-Distribution DetectionPlanningControlStatistics

Questions & Answers

Common questions about this position

Is this internship paid?

Yes, this is a paid 12-week internship opportunity for summer 2026.

What is the work arrangement for this internship?

This internship will be a hybrid in-office role.

What are the key qualifications for this role?

Candidates must be currently pursuing a Ph.D. in Machine Learning, Robotics, or related fields, have publications or a desire to publish at high-impact conferences/journals on relevant topics, and be passionate about large scale challenges in ML grounded in physical systems, especially robotics.

What is the team and focus at Toyota Research Institute?

The Trustworthy Learning under Uncertainty (TLU) team in the Robotics division focuses on enabling the robust and reliable deployment of Large Behavior Models (LBMs) at scale in human environments, working on topics like Policy Evaluation, Failure Detection, Imitation Learning, Reinforcement Learning, and Uncertainty Estimation with a multidisciplinary team.

What makes a strong candidate for this internship?

Strong candidates are Ph.D. students with a track record or strong interest in publishing at top conferences like NeurIPS, ICML, CoRL on topics such as Reinforcement Learning, Uncertainty Estimation, and robotics, and who are passionate about solving large-scale ML challenges in physical systems.

Toyota Research Institute

Research in mobility, safety, and automation

About Toyota Research Institute

Toyota Research Institute focuses on improving mobility through research and development in the automotive and technology sectors. The company works on enhancing safety, automated driving, robotics, materials science, and machine learning. Their products include advanced safety features and automated driving systems that aim to make driving safer and more efficient. Unlike many competitors, TRI emphasizes a research-driven approach, collaborating with various partners and licensing their innovations to enhance Toyota's offerings and maintain a competitive edge. The goal of TRI is to advance mobility solutions that improve quality of life and support the transition to zero-emissions transportation.

Los Altos, CaliforniaHeadquarters

2016Year Founded

$100MTotal Funding

SEEDCompany Stage

Robotics & Automation, Automotive & Transportation, AI & Machine LearningIndustries

201-500Employees

Benefits

Highly competitive benefits package

Robust programs to support the wellbeing, happiness, and health of our people and their families.

401(k) plan including matching and annual profit sharing along with total vacation and holidays totaling 38 days per year.

Subsidized commuter benefits and generous employee and vehicle allowances

2 paid days per year to participate in volunteer activities.

Maternity Leave program with 10 paid weeks plus baby bonding leave and Milk Stork for traveling moms

Baby Bonding Leave–an additional 16 paid weeks–to all new parents, including those who choose to adopt.

Back-up child and adult / elder care programs to help everyone thrive

WellBeats Virtual Fitness Trainer.

Risks

Public skepticism may undermine AI-assisted driving safety initiatives.

Boston Dynamics' high costs could impact TRI's humanoid robot project returns.

Complexity of real-world environments may delay TRI's robot skill deployment.

Differentiation

TRI focuses on AI to enhance human life and mobility.

Partnerships with Boston Dynamics and Stanford showcase TRI's collaborative innovation.

TRI's Driving Sensei concept integrates AI to improve driver skills and engagement.

Upsides

Generative AI allows TRI's robots to learn complex tasks without new coding.

TRI's AI-driven material synthesis could revolutionize EV battery development.

Autonomous tandem drifting collaboration with Stanford pushes vehicle dynamics boundaries.

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