Job description

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). 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. Bonus Qualifications 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. 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 depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment. Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information. TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

Requirements

Must-haves

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.
Please include links to any relevant open-source contributions or technical project write-ups with your application.

Nice-to-haves

Bonus Qualifications

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythonsqlawsci/cd

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave