Senior Data Engineer

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.

• 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.

• 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).
• Deep understanding of data quality, observability, and lineage in high-volume systems.
• Track record of building reliable and performant infrastructure that supports both ad-hoc exploration and repeatable production workflows.

• Experience in AD/ADAS, robotics, or autonomous systems — especially handling perception or planning datasets.
• Familiarity with ML pipeline orchestration frameworks (e.g. Kubeflow, SageMaker, etc).
• Experience working with temporal or spatial data, including geospatial indexing and time-series alignment.
• Exposure to synthetic data generation, simulation logging, or scenario replay pipelines.
• Strong software engineering fundamentals, CI/CD, testing, code review, and service deployment best practices.
• Experience collaborating with cross-functional, distributed teams across research and production orgs.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.