Senior Machine Learning Researcher, Large Behavior Models & Diffusion Policy

Responsibilities

Conduct ambitious research to advance the state-of-the-art in using new capabilities in generative AI (e.g., recent results in diffusion policy [1] , [2] ) for end-to-end perception, planning, and prediction in automated driving with a focus on computer vision as the primary sensing modality.

Research and implement scalable end-to-end architectures that process raw sensor data to generate vehicle trajectories, addressing the challenges of long-tail driving scenarios with low data coverage.

Prototype, validate, and iterate model architectures using imitation learning and large-scale data, ensuring robust performance across diverse scenarios.

Perform closed-loop evaluations in sensor simulations and real-world testing environments to rigorously assess model performance, stability, and scalability.

Explore multi-modal and language-conditioned models to broaden the applicability of end-to-end policies, using external data sources and transfer learning to enhance generalization.

Collaborate with researchers and engineers across TRI, Woven by Toyota, and Toyota’s global ecosystem to accelerate model deployment and evaluation in both controlled environments (closed-course) and public road driving.

Take the lead on writing and publishing research results in peer-reviewed venues.

Qualifications

A PhD or equivalent experience in a robotics-relevant or embodied-AI field such as Computer Science, Mathematics, Physics, or Engineering.

A consistent track record of publishing at high-impact 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 large-scale models, including foundation models (e.g., vision-language models, text-to-video models).

Proficiency in Python and C++ for implementing and evaluating research ideas.

Bonus Qualifications

Experience with robot motion planning techniques like trajectory optimization, sampling-based planning, and model predictive control, or experience with automated driving domains (e.g., perception, prediction, mapping, localization, planning, simulation).

Experience in developing production-level code for real-time operating systems.

Experience optimizing runtime-critical systems for Linux, UNIX-like real-time operating systems on automotive-grade compute platforms, and building safety-critical software architectures.