Senior Data Engineer

• 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.