ML Research Engineer, Interpretable AI for End-to-End Automated Driving

Toyota Research Institute

Job Location:

Los Altos, CA - USA

Yearly Salary: $ 176000 - 253000

Posted on: 8 hours ago

Vacancies: 1 Vacancy

Job Summary

At Toyota Research Institute (TRI) were on a mission to improve the quality of human life. Were developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility weve built a world-class team advancing the state of the art in AI robotics driving and material sciences.

The Team

The Automated Driving Advanced Development (AD2) division at TRI will focus on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products services and needs. We achieve this through partnership collaboration and shared commitment. This new division is leading a new cross-organizational project between TRI and Woven by Toyota to conduct research and develop a fully end-to-end learned driving stack. This cross-org collaborative project is harmonious with TRIs robotics divisions efforts in Diffusion Policy and Large Behavior Models.

Within AD2 we are pursuing a focused research effort in Interpretable AI (iAI) for end-to-end learned automated driving systems tightly coupled with AD2s work on Large Behavior Models (LBM-Drive) and World Foundation Models (WFM) while remaining architecturally and product independent.

The Opportunity

We are seeking a Machine Learning Researcher to contribute to research on interpretable AI methods for learning-based automated driving systems. This role is ideal for a researcher who enjoys hands-on experimentation model development and evaluation and who wants to work on foundational problems at the intersection of autonomy interpretability and safety. You will work closely with senior researchers and engineers to develop methods that make end-to-end neural driving policies more interpretable diagnosable and verifiable while preserving performance and scalability. Your work will contribute to building glass-box representations that help engineers and researchers better understand debug and validate learned driving behaviors.

Responsibilities

Conduct research on interpretable AI methods for end-to-end learned automated driving policies under the guidance of senior and staff researchers.
Develop and evaluate structured representations of driving behavior such as interpretable behavioral modes underlying learned neural policies.
Implement methods that associate driving behavior with perceptual and contextual cues including language-based or symbolic explanations where appropriate.
Design and run experiments using large-scale learned policies and simulation infrastructure to assess interpretability diagnostic value and failure modes.
Contribute to evaluations of explainability methods for debugging validation and analysis of learned driving systems in simulation and/or controlled datasets.
Collaborate with researchers and engineers across AD2 LBM and WFM teams to integrate xAI ideas into broader research workflows.
Document research findings clearly and contribute to internal reports technical presentations and peer-reviewed publications.
Stay up to date with advances in interpretable AI representation learning generative models and embodied AI research.

Qualifications

Masters or PhD or equivalent research experience in Machine Learning Robotics Computer Vision or a related quantitative field.
A demonstrated ability to conduct independent research and contribute to peer-reviewed publications at leading venues (e.g. NeurIPS ICML ICLR CVPR CoRL RSS ICRA).Strong foundation in modern machine learning including deep learning representation learning and sequence or policy modeling.
Experience implementing and evaluating ML models using Python (and familiarity with C in research or experimental contexts).
Interest in or experience with end-to-end learning approaches for robotics or autonomous systems.
Ability to work effectively in collaborative cross-disciplinary research environments.
Strong written and verbal communication skills.

Bonus Qualifications

Experience with interpretable AI or model introspection techniques.
Familiarity with structured or hybrid models (e.g. latent-variable models program induction or discrete representations).
Experience evaluating learning-based systems in closed-loop simulation or real-world embodied settings.
Background in automated driving robotics or safety-critical AI systems.

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

The pay range for this position at commencement of employment is expected to be between $176000 and $253000/year for California-based roles. Base pay offered will depend on multiple individualized factors including but not limited to a candidates experience skills job-related knowledge and market location. TRI offers a generous benefits package including medical dental and vision insurance 401(k) eligibility paid time off benefits (including vacation sick time and parental leave) and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

Please reference thisCandidate Privacy Noticeto 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 applicants 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.

Required Experience: