Senior Data Engineer, Large Behavior Models

About the role

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.

The Automated Driving Advanced Development division at TRI will focus on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. This new division is leading a new cross-organizational project between TRI and Woven by Toyota to conduct research and develop a fully end-to-end learned driving stack. This cross-org collaborative project is harmonious with TRI’s robotics divisions' efforts in Diffusion Policy and Large Behavior Models.

We are looking for a Senior Data Engineer to design and build the foundational data infrastructure and tools that power our autonomy research and development workflows. This includes large-scale ingestion pipelines, structured feature stores, labeling infrastructure, scene search and data discovery tools, and performance diagnostics for machine learning and simulation workflows.

Responsibilities - Design and implement scalable, production-grade pipelines for data ingestion, transformation, storage, and retrieval from vehicle fleets and simulation environments.

- Build internal tools and services for data labeling, curation, indexing, and cataloging across large and diverse datasets.

- Collaborate with ML researchers, autonomy engineers, and data scientists to design schemas and APIs that power model training, evaluation, and debugging.

- Develop and maintain feature stores, metadata systems, and versioning infrastructure for structured and unstructured data.

- Support the generation and integration of synthetic datasets with real-world logs to enable hybrid training and simulation workflows.

- Optimize pipelines for cost, latency, and traceability, ensuring reproducibility and consistency across environments.

- Partner with simulation and cloud platform teams to automate workflows for closed-loop testing, scenario mining, and performance analytics.

Qualifications - Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

- 8+ years of experience building data-intensive software systems, ideally in robotics, autonomous driving, or large-scale ML environments.

- Proficient in Python, SQL, and familiar with C++.

- Experience designing ETL pipelines using modern frameworks (e.g., Apache Spark, Flyte, Union).

- Strong knowledge of cloud-native architectures, including AWS services (e.g., S3, or equivalents (Google Cloud platform)

- Familiarity with sensor data types (camera, lidar, radar, GPS/IMU) and common data serialization formats (e.g., protobuf. ROS2bag, MCAP).