Senior, ML Engineer - Auto Tagging

Location:

Michigan, United States

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

The Auto Tagger team is the engine behind our data flywheel, responsible for translating petabytes of raw, multi-modal vehicle data into a highly curated library of critical driving scenarios. By mining driving logs for long-tail events, we provide the foundational datarequiredfor safe autonomous trucking. Leveraging Pegasus logical layers, this team structures and catalogs findings into an observations database that directly accelerates development across autonomousperception, sensor fusion, and generative simulation testing.

• Scenario Mining at Scale:Architect andoptimizedistributed data pipelines to process massive multi-sensor logs (camera, LiDAR, radar, kinematics), automatically extracting and cataloging safety-critical and long-tail driving events.
• Advanced Event Tagging:Develop and tune both heuristic-based and ML-assisted algorithms (including exploring Vision-Language Models or semantic vector search) to automatically classify and describecomplex environmental and behavioral scenarios.
• Standardized Data Structuring:Extract and format scenario datautilizingthe Pegasus layer standard (alongsideopensourceframeworks) to ensure semantic consistency and rigorous metadata integrity.
• Data Flywheel Integration:Manage the ingestion of tagged events into the observations database, enabling high-speed querying and retrieval for ML training, regression testing, and system validation.
• Cross-Functional Alignment:Operate with broad autonomy to drive consensus across organizational boundaries. Collaborate closely with downstream consumers inperception, simulation, and systems engineering to define what constitutes an "interesting scenario" and operationalize a continuous data loop.
• Mentorship & Team Growth:Guide, mentor, and elevate less-experienced engineers. Lead design reviews,establish coding standards, and foster a culture of technical excellence and collaborative problem-solving.

• Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience OR;
  
  Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience OR;
  PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 1+ years of experience.
• Core
  
  Languages:
  
  Strong Python and SQL skills, with heavy experience processing massive time-series or unstructured datasets.
• ML & Dataset Curation:Hands-on machine learning and dataset curation experience, with a demonstrated history of implementing targeted datasets that measurably improve downstream model performance.
• Data Exploration:Hands-on experience using Databricks (or similar platforms) for large-scale analytics, interactive querying, and making massive vehicle datasets searchable.
• Cloud & Compute:Expertise in distributed compute…