Senior Research Engineer, Computer Vision (LFV/WFM)

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.

Qualifications

• 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.

Bonus Qualifications

• 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.).