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 worldclass team in Energy & Materials HumanCentered AI Human Interactive Driving Large Behavioral Models and Robotics.
The Discover Nurture and Adopt (DNA) division at TRI focuses 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. DNA is leading a new crossorganizational project between TRI and Woven by Toyota to research and develop a fully endtoend learned automated driving / ADAS stack. This crossorg collaborative project is synergistic with TRIs robotics divisions efforts in Diffusion Policy and Large Behavior Models (LBM).
We are looking for a Senior Machine Learning Researcher to join us in developing a stateoftheart pixelstoaction endtoend system for automated driving. As an expert in machine learning you will contribute to designing and developing innovative models for our autonomy stack and deploying them on vehicle platforms to solve daily driving tasks and handle longtail scenarios.
An ideal candidate has a strong track record of leading independent research efforts preferably including mentoring and collaborating with less experienced students and researchers. You will help to drive our exploration into endtoend learning approaches for automated driving using largescale sensor data directly for perception planning and prediction to overcome traditional information bottlenecks. This includes expanding our successful Large Behavior Model (LBM) robotics efforts and Diffusion Policy (DP) research into the driving domain designing scalable architectures and integrating visuallanguageaction modalities. Beyond refining models for closedloop driving on public roads and in simulation you will also explore data quality filtering transfer learning from diverse data sources and edge deployment optimization. This work is part of Toyotas global AI efforts to build a more coordinated global approach across Toyota entities.
Research and implement scalable endtoend architectures that process raw sensor data to generate vehicle trajectories addressing the challenges of longtail driving scenarios with low data coverage.
Prototype validate and iterate model architectures using imitation learning and largescale data ensuring robust performance across diverse scenarios.
Perform closedloop evaluations in sensor simulations and realworld testing environments to rigorously assess model performance stability and scalability.
Explore multimodal and languageconditioned models to broaden the applicability of endtoend policies using external data sources and transfer learning to enhance generalization.
Collaborate with researchers and engineers across TRI Woven by Toyota and Toyotas global ecosystem to accelerate model deployment and evaluation in both controlled environments (closedcourse) and public road driving.
Take the lead on writing and publishing research results in peerreviewed venues.
Qualifications
A PhD or equivalent experience in a roboticsrelevant or embodiedAI field such as Computer Science Mathematics Physics or Engineering.
A consistent track record of publishing at highimpact conferences/journals (CVPR ICLR NeurIPS ICML CoRL RSS ICRA ICCV ECCV PAMI IJCV etc.
A consistent track record of independent research.
Demonstrated ability to independently formulate and complete a research agenda while collaborating across subject areas.
Experience training largescale models including foundation models (e.g. visionlanguage models texttovideo models).
Proficiency in Python and C for implementing and evaluating research ideas.
Bonus Qualifications
Experience with robot motion planning techniques like trajectory optimization samplingbased planning and model predictive control or experience with automated driving domains (e.g. perception prediction mapping localization planning simulation).
Experience in developing productionlevel code for realtime operating systems.
Experience optimizing runtimecritical systems for Linux UNIXlike realtime operating systems on automotivegrade compute platforms and building safetycritical software architectures.
Please add a link to Google Scholar and 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$201600and$302400/year for Californiabased roles; however base pay offered may vary depending on multiple individualized factors including market location jobrelated 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 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.
Disclaimer: Drjobpro.com is only a platform that connects job seekers and employers. Applicants are advised to conduct their own independent research into the credentials of the prospective employer.We always make certain that our clients do not endorse any request for money payments, thus we advise against sharing any personal or bank-related information with any third party. If you suspect fraud or malpractice, please contact us via contact us page.