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 Interactive Driving, Human-Centered AI, Energy and Materials, Large Behavior Models, and Robotics.

The Team

Within the Extreme Performance Intelligent Control department of the Human Interactive Driving division, we are developing techniques to advance vehicle safety and performance capabilities using tools from machine learning, computer vision, and optimal control. We are pursuing a cooperative approach to high-performance driving by developing predictive models and control frameworks for extreme vehicle maneuvering that match or exceed the skills of expert drivers. We envision a world where these tools allow each vehicle to support its driver, creating a safe and enjoyable experience for all.

The Opportunity

We are looking for a data-driven robotics or driving researcher with a strong background in embodied machine learning and a “make it happen” mentality who is interested in spending a year working in our research team as a postdoc to drive the state-of-the-art in model predictive control and reinforcement learning for autonomous driving in highly dynamic scenarios. The ideal candidate is able to operate independently when needed, but works well as part of a larger integrated group at the groundbreaking edge of robotics and machine learning. Candidates with experience working with agile robots or other embodied systems (such as autonomous vehicles or UAV’s) and dealing with real-world sources of uncertainty are strongly preferred.

The Role

An ideal candidate would have a strong track record of leading independent research efforts, preferably including mentoring and collaborating with less experienced students and researchers. A current postdoc or graduating Ph.D student looking for an additional year of research experience before beginning a faculty position would be a great fit for this role. This position requires a hands-on researcher who has curiosity about formulating new research ideas, implementing and evaluating them on vehicles, and working in a highly collaborative environment. We expect this position to result in strong peer-reviewed publications, ultimately leading to a potential longer-term collaboration with TRI.

Responsibilities

Conduct ambitious research to advance the state-of-the-art in decision-making and control for autonomous systems operating at their performance limits in the face of environmental, perceptual, and system uncertainty
Work with high-performance machine learning pipelines and deploy models and algorithms to hardware for closed-loop evaluation with scalability, efficiency, and performance in mind
With your teammates, test your methods on our high-performance autonomous vehicles at proving grounds, race tracks, and other test facilities
Collaborate with a multidisciplinary team at TRI and our university partners
Be a key member of the team and play a critical role in rapid progress measured by both the development of internal capabilities and high-impact external publications

Qualifications

Ph.D. or equivalent experience in a relevant technical field (e.g. computer science, robotics, mechanical engineering, aerospace engineering)
Consistent track record of publishing relevant research (RL, MPC, IL, or their combination) at high-impact conferences and journals (CoRL, RSS, ICRA, RLC, ICLR, ICML, NeurIPS, etc.)
Experience deploying models on embodied systems
Extensive practical experience with a major machine learning framework such as PyTorch or TensorFlow
Familiarity with data pipelines, model serving and optimization, cloud training, and dataset management is also useful
Ability to independently formulate and implement a research agenda while collaborating with other researchers and engineers across a spectrum of disciplines
Sufficient software-engineering proficiency to implement and evaluate research ideas, particularly strong proficiency in Python
Experience with high-fidelity vehicle dynamics and sensor simulation is a major plus
An ability to adapt quickly and switch between modes of rapid prototyping and robust implementation as required

Please add a link to Google Scholar and include a full list of publications when submitting your CV to this position.

The pay range for this position at commencement of employment is expected to be between $165,760 and $207,200/year for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave) and an annual cash bonus structure. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information.

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.