Postdoctoral AI Fellow, Post-Training for Materials Discovery

Toyota Research Institute • Los Altos, CA

10d • $144,000 - $207,000

About The Position

Toyota Research Institute (TRI) is seeking a postdoctoral AI Fellow to define and pursue original research 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 own work from problem framing through validation and help shape the research as it evolves. A materials-science background is not required; curiosity and willingness to learn from domain experts are important.

The fellow will join TRI’s Accelerated Materials Design and Discovery (AMDD) program, which combines AI, autonomy, experimental data, and computational modeling to accelerate materials discovery for emissions-free mobility. The role involves close collaboration with materials scientists, machine-learning researchers, and software engineers to turn research ideas into capabilities that inform real-world decisions. An academic AI co-mentor is intended, with the specific collaboration developed after joining TRI and subject to institutional arrangements. Publications and conference presentations are encouraged and expected. Projects may use internal datasets relevant to Toyota applications, subject to confidentiality and publication constraints, alongside open-data research. The broader goal is to advance fundamental AI while addressing scientific and industrial challenges.

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, with experience in 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, including holiday pay and sick time

What This Job Offers

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