Research Scientist - Energy and Materials

Los Altos, CA /
Energy & Materials – Energy & Materials /
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 in human-centric AI, energy and materials, machine learning, and robotics.

The Challenge
The long-term vision of TRI’s Accelerated Materials Design and Discovery (AMDD) program is to accelerate the development of truly emissions-free mobility. Realizing this vision will require the discovery of new materials for batteries, fuel cells, and more. Our aim at TRI is to merge sophisticated computational materials modeling, new experimental data, artificial intelligence, and automation to significantly accelerate materials research. Our focus is on developing tools and capability to enable this acceleration. We collaborate closely with a dozen universities and national labs and our colleagues in Japan. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community more broadly.

The Opportunity
We are looking for a research scientist who can help us develop innovative approaches to accelerate materials discovery and their experimental realization. Right now, we need help with creating AI approaches to data analysis and autonomous decision-making. You will develop new machine learning models that incorporate scientific theory or intuition. You will collaborate with software engineers to translate your research into prototype tools that can be used by the wider community. We all get opportunities to start new seedling projects that are focused on prototyping new innovative approaches. We regularly get to present our work at conferences and within the Toyota family of companies, patent our innovations, and publish papers in high-impact journals.

The Team
We encourage you to join a creative team of scientists and engineers dedicated to enabling a sustainable future. You will be part of the Energy and Materials (E&M) division which is accelerating Toyota’s path to carbon neutrality. In addition to our work on accelerating materials discovery, the division builds tools for battery manufacturing and provides strategic advice on carbon-neutral pathways and technologies. We collaborate with colleagues from other teams at TRI with backgrounds in robotics, behavioral science, and computer science. We all grow working alongside other inspiring people, and constantly learn new skills together at the interface of materials science and AI. 


    • Have a PhD in physics, chemistry, chemical engineering, materials science, applied mathematics/computer science, or a related field.
    • Have a strong research background, including peer-reviewed publications.
    • Are proficient at a general-purpose programming language (preferably Python).
    • Have effective written and verbal communication skills.
    • Have demonstrated curiosity about machine learning.
    • Are familiar with or want to learn practices for team-based software development.
    • Thrive in a culture that values diversity, collaboration, humility, and learning.

Bonus Qualifications:

    • Have experience with reinforcement learning, Generative Adversarial Networks and/or uncertainty quantification.
    • Are comfortable using a common deep learning framework.
    • Are comfortable working with materials and scientific data.
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TRI is committed to the safety of our workforce and community. Thereby, we require COVID-19 vaccinations, including boosters, for all employees and applicants. Applicants are considered fully vaccinated two weeks after their second dose in a 2-dose series, such as the Pfizer or Moderna vaccines, or two weeks after a single-dose vaccine, such as Johnson & Johnson’s Janssen vaccine. Any offer of employment will be conditioned upon, among other things, vaccination status against COVID-19. TRI provides reasonable accommodations to our COVID-19 vaccination policy in accordance with applicable state, federal and local laws.