Senior Research Engineer, Computer Vision (LFVWFM)
Job Location:
Los Altos, CA - USA
Yearly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
At Toyota Research Institute (TRI) were on a mission to improve the quality of human life. Were developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility weve built a world-class team advancing the state of the art in AI robotics driving and material sciences.
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.
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.
Masters 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).
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.
Required Experience:
Senior IC
About Company
Please include links to any relevant open-source contributions or technical project write-ups with your application. The pay range for this position at commencement of employment is expected to be between $180,000 and $258,750/year for California-based roles. Base pay offered will dep ... View more
