Human-Centered AI Research Intern, Causal Reasoning Models (Summer 2027)

The Team

This internship opportunity falls within the Adaptive Behavior Systems team of the 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.

The Internship

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 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 Summer 2027 paid 12-week internship opportunity and is a hybrid in-office role.

Responsibilities

• Perform research and publish in a relevant venue (e.g., NeurIPS, ICML, ICLR, CLeaR, UAI, COLM, ACL, EMNLP, TMLR; findings may be presented at ACIC or EuroCIM).
• 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.

Qualifications

• 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.

Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.