The Learning From Videos (LFV) team develops world foundation models that leverage large-scale multi-modal data (RGB, depth, flow, semantics, actions, tactile, audio, etc.) from multiple domains to power downstream embodied AI tasks. Our topics of interest include Video Generation, World Models, 4D Reconstruction, Multi-Modal Models, Multi-View Geometry, Data Augmentation, and Video-Language-Action models, with a primary focus on embodied applications such as robotics and driving. We are making progress on some of the hardest scientific challenges around spatio-temporal reasoning, and how it can lead to the deployment of autonomous agents in real-world unstructured environments.

 

Our team is looking for a Research Engineer to help develop and deploy our world foundation models (WFMs) toward their key milestones in the autonomous driving domain. As our WFMs scale in both capability and ambition, we need a strong engineer who can bridge the gap between research ideas and production-grade systems. This is not a traditional software engineering role; you will work directly alongside research scientists, understand the research deeply enough to make independent technical decisions, and play a key role in enabling the deployment of key research breakthroughs into close-to-production environments.

 

As a Research Engineer, you will be responsible for supporting and optimizing large-scale distributed training of diffusion and transformer models; maintaining the infrastructure that ingests, unifies, and serves heterogeneous multi-modal datasets at scale; and developing tools and pipelines that accelerate the research-to-results cycle. You will work closely with researchers to prototype new ideas, run experiments, and help ship our most successful models toward real-world applications with real-world impact.

This role requires close collaboration with multiple TRI divisions (Robotics, Automated Driving, Human-Interactive Driving, etc.) as well external Toyota and University partners, and the ability to reconcile and prioritize possibly competing requirements in a fast-paced combination of research and production environments.