Future Product Innovation Research Intern, Human-AI Computational Design

The Team

This internship opportunity falls within the Human-Centered AI Division (HCAI). We are an integrated team of ML researchers, behavior scientists, and human-computer interaction experts. At the core of our work, we aim to create purposeful human futures enabled through technology.

The Internship

We are seeking a highly motivated PhD research intern to join our Future Product Innovation team and advance the frontiers of Generative AI for automotive product design. The ideal candidate will have a strong background in Machine Learning or a related field, with a demonstrated interest in developing rigorous quantitative and human-centered methods to evaluate and improve generative AI systems. In this project, the intern will explore models for 3D parametric design generation and reasoning, with a particular focus on evaluating design quality, intent alignment, and decision-making under complex constraints and trade-offs.

Over the course of the project, in addition to Future Product Innovation group meetings, the intern will also participate in all Human-Centered AI division meetings and meetings with stakeholders from relevant Toyota business groups. The intern will be exposed to how interdisciplinary industrial research works and learn from our team as well as the other interns in our division. Furthermore, the intern will engage in strategy discussions about how their research connects to business impact at Toyota.

Responsibilities

In collaboration with your mentor and the other researchers in the department, you will:

• Scope the project to align to the core research efforts;
• Be the primary driver of the technical plan (e.g., model development, analysis plan) with regular feedback from mentors;
• Execute the project using TRI resources; and
• Present the project’s approach and findings in research meetings.

Qualifications

• Currently enrolled in a PhD program in Machine Learning, Computer Science, Mechanical Engineering, or related fields.
• Experience with 3D parametric design, deep generative models, and relevant tools (e.g., build123d, Blender, pythonocc-core).
• Experience developing subjective evaluation methods for generative and predictive models.
• Track record of executing research projects including publications at top venues including but not limited to NeurIPS, EMNLP, ACL, ICML.
• Desire to work on challenging open-ended research projects.
• Demonstrated ability to work autonomously while soliciting feedback.
• Excellent communication and teamwork skills.

Preferred Qualifications

• Experience adapting models to specific tasks through post-training techniques.
• Strong engineering skills, with the ability to independently prototype research solutions.