Senior Research Scientist

Toyota Research Institute•Los Altos, CA

2d•Onsite

About The Position

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 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 and devices 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 colleagues across global Toyota, as well as with universities and national labs. AMDD seeks to develop and translate the newest technologies into practice, both within Toyota and the open research community. We are hiring a Senior Research Scientist to help deepen the chemistry at the core of our materials-design models. This is a full-time role with the scope to take problems from framing through to validation. We are looking for researchers with expertise in uncovering the chemical relationships that govern complex materials and drive their discovery. Our goal is to integrate this fundamental chemical intuition as a primary, first-class input within our models, moving beyond post-hoc analysis. The team is currently developing representations where internal variables are engineered to represent chemical meaning, rather than only statistical correlations. We are ready to expand this vision with a scientist who can further sharpen the underlying chemistry of our approach. In this role, you will work alongside ML researchers and software engineers to translate these insights into practical tools that drive real-world materials decisions. You will have the opportunity to publish your findings in leading venues across both materials chemistry and the AI-for-materials community. Join us in defining a future where chemical heuristics are central to modern, AI-driven materials discovery. You will expand your professional network, as part of a creative team of scientists and engineers dedicated to enabling a sustainable future. Our team prioritizes learning new skills together at the interface of materials science and AI. You will be part of the Energy & Materials division which is accelerating Toyota’s path to carbon neutrality. In addition to our work on accelerating materials discovery, the division develops capabilities for AI assisted manufacturing, AI technologies to scale from materials to devices, and provides strategic analysis on carbon neutral pathways and technologies.

Requirements

Hold a PhD in solid-state/inorganic chemistry, materials chemistry, materials science, condensed-matter physics, or a related field.
Have a strong publication record using chemical reasoning to predict or design functional inorganic materials.
Have hands-on experience with at least one of: solid-state synthesis, structural characterization, electrochemistry, or first-principles workflows.
Are excited to translate fundamental chemical understanding into practical tools that address real industrial problems.
Thrive in a culture that values diversity, collaboration, humility, and learning.

Responsibilities

Develop chemically grounded hypotheses that guide materials discovery and help clarify which scientific relationships matter most.
Translate fundamental chemical intuition into scientific inputs, constraints, evaluation criteria, and validation strategies for AI-assisted materials workflows.
Analyze complex inorganic and functional materials systems to identify structure-property relationships relevant to batteries, fuel cells, and other emissions-free mobility technologies.
Partner with ML researchers and software engineers to ensure that materials-design tools reflect sound chemical reasoning and are scientifically meaningful.
Design and assess workflows that connect computational materials modeling, experimental data, literature evidence, and AI-assisted prediction or optimization.
Evaluate model outputs using domain knowledge, first-principles calculations, experimental data, or other appropriate scientific benchmarks.
Communicate scientific insights clearly through internal technical discussions, cross-functional collaborations, publications, and presentations.
Collaborate with colleagues across TRI, Toyota, universities, and national labs to translate emerging materials-discovery methods into useful research capabilities.