Senior Machine Learning Researcher, Large Behavior Models & Diffusion Policy

Toyota Research Institute

Los Altos, California

Full Time

Posted March 26, 2025

$200k - $288k

Not Specified

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Job Description

A senior machine learning researcher responsible for developing end-to-end, large-scale models for automated driving systems, focusing on perception, planning, and prediction using advanced AI techniques such as diffusion policies and large behavior models.

Key Responsibilities

Design and develop innovative machine learning models for automated driving systems.
Deploy models on vehicle platforms to address daily driving tasks and long-tail scenarios.
Lead research efforts in end-to-end learning approaches using large-scale sensor data for perception, planning, and prediction.
Expand and integrate Large Behavior Model (LBM) and Diffusion Policy (DP) research into autonomous driving applications.
Design scalable architectures and incorporate visual-language-action modalities into models.
Refine models for closed-loop driving in both real-world and simulated environments.
Explore data quality filtering, transfer learning from diverse data sources, and optimize models for edge deployment.

Requirements

Experience in designing and developing innovative machine learning models for autonomous driving systems, including deploying models on vehicle platforms to solve daily driving tasks and handle long-tail scenarios.
A strong track record of leading independent research efforts in machine learning, preferably including mentoring and collaborating with less experienced students and researchers.
Expertise in end-to-end learning approaches for automated driving, utilizing large-scale sensor data directly for perception, planning, and prediction to overcome traditional information bottlenecks.
Experience in expanding Large Behavior Model (LBM) robotics efforts and Diffusion Policy (DP) research into the driving domain, including designing scalable architectures and integrating visual-language-action modalities.
Proficiency in refining models for closed-loop driving on public roads and in simulation.
Experience in exploring data quality filtering, transfer learning from diverse data sources, and edge deployment optimization for autonomous driving models.
Educational background in a relevant field such as Computer Science, Electrical Engineering, Robotics, or related disciplines (implied by the need for research and model development expertise).