Senior Autonomy Data Engineer

Torc Robotics is a leader in autonomous driving technology, focusing on developing software for automated trucks. They are seeking a Senior Autonomy Data Engineer to design and operate the data infrastructure that supports their autonomy program, ensuring reliable data pipelines and effective collaboration with cross-functional teams.

Responsibilities:

• Own the design and organization of the program’s data lake, including schema definitions, partitioning strategy and metadata indexing
• Design and maintain end-to-end pipelines that ingest high-bandwidth sensor logs from vehicles into cloud storage with high reliability and tolerant of ad-hoc and intermittent connectivity mechanisms
• Develop data validation and integrity checks that can detect corrupted information, missing sensors, and inconsistent calibration prior to the data being processed by downstream systems
• Implement retention, tiering and lifecycle policies for data to balance storage costs with development value
• Build tooling to query raw logs to produce curated training and evaluation datasets
• Build automation to run cost-effective pseudo-labeling workflows at the scale of data ingest
• Implement data quality and model performance metrics that are used to direct labeling effort toward the highest-value examples
• Deploy and maintain data visualization tooling to support log review, annotation QA, and autonomy debugging workflows
• Build integrations between the visualization tooling and the data lake so engineers can navigate from a dataset entry or model failure directly to the origin log data
• Work with autonomy engineers to define and surface custom visualization panels and implement metrics for analyzing unstructured operating environments
• Build dashboards that provide the autonomy engineers visibility into data coverage by terrain type, operating environment and geographic region
• Establish and document data contracts between the data services and model training consumers
• Partner with perception, planning and embedded engineers across the data lifecyle: from shaping the logging schemas and collection triggers to defining the dataset interfaces that supply model training and evaluation
• Define data engineering standards, best practices, and tooling choices for an innovative and fast-paced team
• Contribute to the data roadmap and provide input to technical leadership on investment priorities
• Mentor junior engineers and raise the team’s capabilities in data infrastructure scalability and operational hygiene

Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Electrical Engineering or a related field with 6+ years of data engineering experience or a Master's with 4+ years
• Strong proficiency in Python and SQL, with demonstrated ability to build production-quality data pipelines
• Deep experience with cloud data infrastructure (AWS preferred: S3, Glue Athena, redshift, or equivalent) and infrastructure-as-code tools (Terraform, Cloud Formation)
• Solid understanding of data partitioning strategies and columnar storage formats (Parquet, Orc, etc.)
• Experience building and operating data pipelines that process time-series and binary data
• Proven ability to evaluate and integrate open-source tooling when appropriate versus building from scratch
• Strong instincts for delivering data quality through first-class implementations of monitoring, validation and lineage tracking
• Experience with autonomous vehicles, robotics, or other sensor-driven autonomous systems
• Deep experience with Foxglove or Rerun beyond basic playback, e.g. building custom extensions or integrating them into a structured log review or annotation QA workflow
• Familiarity with the MCAP CLI and/or python library and experience converting MCAP data to columnar data formats for further querying and processing
• Experience with data curation for ML training, e.g. diversity sampling, pseudo-labeling, and dataset versioning