About this Postdoctoral AI Fellow, Post-Training for Materials Discovery role at Tri
At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to 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.
The Team
The goal of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to enable the materials breakthroughs needed for truly emissions-free mobility. We combine AI, autonomy, experimental data, and computational modeling to pursue a step change in the speed and scale of materials discovery. We collaborate closely with colleagues at universities, national labs, and global Toyota. Together, we develop capabilities designed for real-world impact in both Toyota’s industrial applications and open research more broadly.
The Opportunity
We are looking for a postdoctoral AI Fellow to define an original research direction in model post-training, using materials discovery as a real-world testbed. This appointment is anticipated to be for one year, with an option to extend another year by mutual agreement. You will take ownership of problem framing through validation and play a leading role in shaping the research as it evolves. No materials background is required, only the curiosity to learn from domain experts and uncover where AI can make a meaningful difference.
As part of AMDD, you will work closely with materials scientists, ML researchers, and software engineers to translate your ideas into capabilities that can inform real-world decisions. The fellowship is also intended to include an academic co-mentor in AI, with the specific collaboration developed with you after you join TRI, subject to institutional arrangements. Publications and conference presentations are encouraged and expected. Projects may also draw on internal datasets relevant to Toyota use cases. Ultimately, the goal is to pursue fundamental advances in AI while addressing important scientific and industrial challenges.
Responsibilities
Define and pursue an original research direction in model post-training, using materials discovery as a scientifically meaningful testbed.
Develop and evaluate new post-training methods, which may involve 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.
Qualifications
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.
Demonstrated ability to design, implement, and debug readable, modular, and reproducible machine learning research code in Python, with experience in PyTorch or a comparable framework.
Thrives in a culture that values diversity, collaboration, humility, and learning.
Bonus Qualifications
Experience applying machine learning to scientific or engineering problems or collaborating with domain experts.
