Toyota Research Institute

Postdoctoral AI Fellow, Post-Training for Materials Discovery

Toyota Research Institute  •  $144k - $207k/yr  •  Los Altos, CA (Onsite)  •  16 days ago
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Job Description

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

Qualifications

Bonus Qualifications

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

The pay range for this position at commencement of employment is expected to be between $144,000 and $207,000/year for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, and paid time off benefits (including holiday pay and sick time). Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

Toyota Research Institute

About Toyota Research Institute

At Toyota Research Institute (TRI), we’re conducting research to amplify human ability, focusing on making our lives safer and more sustainable. Led by Dr. Gill Pratt, TRI’s team of researchers develops technologies to advance automated driving, energy and materials, human-centered artificial intelligence, human interactive driving, large behavior models, and robotics. We’re dedicated to building a world of “mobility for all” where everyone, regardless of age or ability, can live in harmony with technology to enjoy a better life. Through innovations in AI, we will:

- Develop technology for vehicles and robots to help people enjoy new levels of independence, access, and mobility.

- Bring advanced mobility technology to market faster.

- Discover new materials that will make batteries and hydrogen fuel cells smaller, lighter, less expensive, and more powerful.

Our work is guided by a dedication to safety – in how we research, develop, and validate the performance of vehicle technology to benefit society. As a subsidiary of Toyota, TRI is fueled by a diverse and inclusive community of people who carry invaluable leadership, experience, and ideas from industry-leading companies. Over half of our technical team holds PhD degrees. We’re continually searching for the world’s best talent ‒ people who are ready to define the new world of mobility with us!

We strive to build a company that helps our people thrive, achieve work-life balance, and bring their best selves to work. At TRI, you will have the opportunity to enjoy the best of both worlds ‒ a fun start-up environment with brilliant people who enjoy solving tough problems and the financial backing to successfully achieve our goals. Come work with TRI if you’re interested in transforming mobility through designing safer cars, enabling the elderly to age in place, or designing alternative fuel sources. Start your impossible with us.

Industry
Biotech & Life Sciences
Company Size
201-500 employees
Headquarters
Los Altos, California
Year Founded
2016
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