Senior Research Engineer, Computer Vision (LFV/WFM)

Job description

Job responsibilities

• Collaborate directly with research scientists to implement, iterate on, and evaluate new architectures, objectives, datasets, and training strategies.
• 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…
• Support and optimize large-scale distributed training of world foundation models on multi-GPU and multi-node clusters.
• 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.

Job 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.
• Experience with cloud training infrastructure (AWS SageMaker, EC2) and containerized workflows (Docker, Kubernetes).