Human-Centered AI Research Intern, Causal Reasoning Models

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 in Human-Centered AI, Large Behavior Models, Robotics, Automated Driving, Energy & Materials and Human Interactive Driving.

This internship opportunity falls within the Adaptive Behavior Systems team of Human-Centered AI Division (HCAI). We are an integrated team of ML researchers and social and behavior scientists. At the core of our work, we aim to enable decisions through human-centered AI that advance collective well-being for people and the planet.

We are seeking a highly motivated and talented PhD research intern to join our adaptive behavior systems team to push the boundaries of causal reasoning in LLMs. The intern will collaborate with our interdisciplinary team of machine learning, behavioral, and social scientists to study how well language-based generative machine learning models can reason causally and to develop methods for testing and improving causal reasoning. The project will be focused on researching in a specific sub-area of the overall goal, towards publication in a top-tier venue. The intern will further engage in strategy discussions about how the research connects to business impact at Toyota.

This is a Winter 2027 paid 12-week internship opportunity. Please note that this internship will be a hybrid in-office role.

Perform research and publish in a relevant venue. Publication target venues include NeurIPS, ICML, ICLR, CLeaR, UAI, and COLM, with ACL or EMNLP where the contribution is primarily linguistic, and TMLR as a journal option. Findings may also be presented at ACIC or EuroCIM. The exact topic is to be finalized with the mentor.

Take ownership of the project from project inception and ideation to validation of the developed methods.

Collaborate cross-functionally with researchers in multiple fields to research and develop technology that leverages generative AI to understand human behavior.

Ph.D. student in related fields - AI/ML, computer science, data science, statistics, or related field.

Publication record or demonstrated research experience in causal inference (structural causal models, identification, counterfactual reasoning).

Hands-on experience with one or more of: LLM evaluation and benchmarking; mechanistic interpretability; and methods for improving LLM reasoning, including prompting and prompt optimization, post-training (supervised fine-tuning or reinforcement learning with verifiable rewards), pretraining, or architectural changes.

Experience with Python programming and DL frameworks like Pytorch.

Interest in human-centered research (e.g. behavioral science, computational social science), including qualitative and/or quantitative methods such as experiments and user studies.

Excellent communication and teamwork skills.