Human-Centered AI Research Intern, Causal Reasoning Models

About the role

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life by developing new tools and capabilities that amplify the human experience. To lead this transformative shift in mobility, we’ve built a world‑class team advancing the state of the art in AI, robotics, driving, and material sciences. This internship sits within the Adaptive Behavior Systems team of the Human‑Centered AI Division (HCAI), an integrated group of ML researchers and social and behavior scientists focused on enabling decisions through human‑centered AI that advance collective well‑being for people and the planet. As a PhD research intern, you will join this team to push the boundaries of causal reasoning in large language models, collaborating with interdisciplinary experts to study and improve how language‑based generative models reason causally, with the goal of publishing results in a top‑tier venue and connecting the research to business impact at Toyota.

Responsibilities

• Perform research and aim to publish in a relevant venue (target venues include NeurIPS, ICML, ICLR, CLeaR, UAI, COLM, ACL/EMNLP for linguistic contributions, TMLR as a journal option; findings may also be presented at ACIC or EuroCIM).
• Take ownership of the project from inception and ideation through 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 AI/ML, computer science, data science, statistics, or a 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; methods for improving LLM reasoning (prompting and prompt optimization, post‑training via supervised fine‑tuning or reinforcement learning with verifiable rewards, pretraining, or architectural changes).
• Experience with Python programming and deep‑learning frameworks such as PyTorch.
• Interest in human‑centered research (behavioral science, computational social science), including qualitative and/or quantitative methods such as experiments and user studies.
• Excellent communication and teamwork skills.

Pay

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 may vary depending on multiple individualized factors, including market location, job‑related knowledge, skills, and experience.

Benefits

• Generous benefits package including vacation and sick time.
• Medical, dental, and vision insurance.
• Paid time off benefits (including holiday pay and sick time).

Schedule

This is a Winter 2027 paid 12‑week internship opportunity. The role is hybrid, requiring in‑office presence.