Senior, ML Engineer - Auto Tagger

Torc Robotics is a leader in autonomous driving technology focused on developing software for automated trucks. The Senior ML Engineer - Auto Tagger will be responsible for architecting and optimizing data pipelines, developing ML-assisted algorithms, and collaborating across teams to enhance data curation for autonomous trucking.

Responsibilities:

• Scenario Mining at Scale: Architect and optimize distributed 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 describe complex environmental and behavioral scenarios
• Standardized Data Structuring: Extract and format scenario data utilizing the Pegasus layer standard (alongside opensource frameworks) 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 in perception, 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

Requirements:

• BS or MS in Computer Science, Robotics, Engineering, or a STEM field, with 6+ years in data engineering, ML systems, or autonomous data curation
• 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 frameworks (Ray, Spark, Beam) and cloud platforms (AWS, GCP, or Azure) for executing heavy data workloads
• AV Standards: Experience parsing complex data formats and applying scenario-description standards like Pegasus layers
• Communication: Exceptional ability to translate complex data engineering challenges into clear strategies for cross-functional stakeholders
• Technical Leadership: Proven track record of mentoring teams, driving system architecture, and defining engineering roadmaps
• Auto-labeling & VLMs: Familiarity with foundational models, auto-labeling pipelines, or zero-shot classification for scenario extraction
• Model Serving: Experience with vLLM, SGLang, or similar frameworks for highly optimized, high-throughput model serving and inference
• Semantic Inference: Experience with semantic extraction and attribute mapping to help build out a robust semantic inference engine, moving beyond standard bounding-box object detection
• Data Tooling: Familiarity with parsing robotics formats (ROS bags, MCAP) and optimizing high-performance columnar storage formats (Parquet, Arrow)
• Downstream Integration: Knowledge of how scenario data feeds into generative simulation workflows, neural rendering, or sensor fusion validation
• Advanced Retrieval: Experience building semantic retrieval systems or vector databases for automotive data