Senior Research Engineer, Computer Vision (LFV/WFM)

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. 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.

Responsibilities

Collaborate directly with research scientists to implement, iterate on, and evaluate new architectures, objectives, datasets, and training strategies. Translate research prototypes into clean, maintainable, reusable code that will be shared across multiple TRI teams and the broader Toyota ecosystem; Build and maintain scalable pipelines for ingesting, converting, validating, and serving heterogeneous datasets (multi-view, multi-modal, multi-embodiment, etc.), across robotics and autonomous driving, into unified training-ready formats. Track and integrate new public and internal datasets as they become available; Support and optimize large-scale distributed training of world foundation models on multi-GPU and multi-node clusters. Manage experiment workflows, profiling, debugging, and hyperparameter sweeps to ensure optimal performance in a timely manner; Develop tools for dataset inspection, experiment tracking, model evaluation, GPU resource management, and visualization. Automate repetitive workflows to improve team velocity; Work with other TRI teams and Toyota affiliates to set up shared pipelines, onboard their data, and support joint training and evaluation efforts; Produce maintainable, well-documented code. Contribute to internal tooling and open-source releases to the scientific community

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field, with a minimum of 3 years of relevant experience and strong software engineering skills; Deep proficiency in Python, PyTorch, and the Unix/Linux toolchain; Comfort working in terminal-heavy, SSH-based workflows on shared GPU clusters; Hands-on experience with large-scale deep learning training, including distributed training (DDP, FSDP, DeepSpeed, or similar), GPU profiling, and debugging training failures at scale; Experience building data pipelines for heterogeneous or multi-modal datasets (images, video, depth, point clouds, actions, etc); Experience with video diffusion models, 3D/4D reconstruction, and multi-view geometry; You are proactive, self-directed, and comfortable operating with ambiguity in a research-driven environment that spans multiple divisions; You are a reliable teammate who communicates clearly and takes ownership of problems end-to-end

Nice to have

Experience with cloud training infrastructure (AWS SageMaker, EC2) and containerized workflows (Docker, Kubernetes); Familiarity with standard data formats and collection pipelines (ROS, MCAP, HDF5, etc.) as well as simulation environments; Proficiency with modern AI-assisted development tools (e.g., Copilot, Cursor, Claude Code) for accelerating engineering workflows; Track record of contributions to open-source projects or publications at top venues (CVPR, ICLR, NeurIPS, RSS, ICRA, etc.) is a plus but not required

Benefits

Medical insurance; Dental insurance; Vision insurance; 401(k) eligibility; Paid time off (vacation, sick time, and parental leave); Annual cash bonus structure