Postdoctoral AI Fellow, Post-Training for Materials Discovery

Toyota Research Institute • Los Altos, CA

6d

About The Position

Toyota Research Institute’s Accelerated Materials Design and Discovery (AMDD) program combines AI, autonomy, experimental data, and computational modeling to accelerate materials discovery for emissions-free mobility. The Postdoctoral AI Fellow will define and pursue an original research direction in model post-training, using materials discovery as a real-world scientific testbed. The appointment is anticipated to last one year, with an option to extend for another year by mutual agreement.

The Fellow will lead research from problem framing through validation and help shape the research as it evolves. Working closely with materials scientists, machine learning researchers, and software engineers, the Fellow will develop capabilities that can inform real-world decisions. The fellowship is intended to include an academic co-mentor in AI, with the collaboration developed after joining TRI and subject to institutional arrangements. Publications and conference presentations are encouraged and expected. Projects may also use internal datasets relevant to Toyota applications, subject to confidentiality and publication constraints. No materials-science background is required; curiosity and a willingness to learn from domain experts are expected.

Requirements

• Ph.D. in machine learning, computer science, statistics, or a related field, obtained within the past three years.
• Expertise in model post-training or a closely related area, such as reinforcement learning, supervised fine-tuning, evaluation, or agentic systems.
• Strong record of original AI or machine learning research, demonstrated through publications, open-source work, or other significant research contributions.
• Ability to identify important research questions, turn open-ended ideas into well-scoped projects, and design rigorous experiments and evaluations.
• Ability to design, implement, and debug readable, modular, and reproducible machine learning research code in Python.
• Experience with PyTorch or a comparable framework.
• Ability to thrive in a culture that values diversity, collaboration, humility, and learning.

Nice To Haves

• Experience applying machine learning to scientific or engineering problems or collaborating with domain experts.

Responsibilities

• Define and pursue an original research direction in model post-training, using materials discovery as a scientifically meaningful testbed.
• Develop and evaluate post-training methods, potentially involving reinforcement learning, data generation and selection, evaluation, or agentic workflows.
• Build robust, reusable research prototypes that extend beyond proof of concept.
• Publish research using open-data use cases and present findings at major AI and scientific conferences.
• Apply capabilities to Toyota-relevant use cases while respecting confidentiality and publication constraints.
• Contribute expertise to related efforts across AMDD.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• Paid time off
• Holiday pay
• Sick time

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree