Senior, ML Engineer - Auto Tagging

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 data required for safe autonomous trucking. Leveraging Pegasus logical layers, this team structures and catalogs findings into an observations database that directly accelerates development across autonomous perception, sensor fusion, and generative simulation testing.

Responsibilities

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; 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; Extract and format scenario data utilizing the Pegasus layer standard (alongside opensource frameworks) to ensure semantic consistency and rigorous metadata integrity; Manage the ingestion of tagged events into the observations database, enabling high-speed querying and retrieval for ML training, regression testing, and system validation; 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; 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; Strong Python and SQL skills, with heavy experience processing massive time-series or unstructured datasets; Hands-on machine learning and dataset curation experience, with a demonstrated history of implementing targeted datasets that measurably improve downstream model performance; Hands-on experience using Databricks (or similar platforms) for large-scale analytics, interactive querying, and making massive vehicle datasets searchable; Expertise in distributed compute frameworks (Ray, Spark, Beam) and cloud platforms (AWS, GCP, or Azure) for executing heavy data workloads; Experience parsing complex data formats and applying scenario-description standards like Pegasus layers; Exceptional ability to translate complex data engineering challenges into clear strategies for cross-functional stakeholders; Proven track record of mentoring teams, driving system architecture, and defining engineering roadmaps

Nice to have

Familiarity with foundational models, auto-labeling pipelines, or zero-shot classification for scenario extraction; Experience with vLLM, SGLang, or similar frameworks for highly optimized, high-throughput model serving and inference; Experience with semantic extraction and attribute mapping to help build out a robust semantic inference engine, moving beyond standard bounding-box object detection; Familiarity with parsing robotics formats (ROS bags, MCAP) and optimizing high-performance columnar storage formats (Parquet, Arrow); Knowledge of how scenario data feeds into generative simulation workflows, neural rendering, or sensor fusion validation; Experience building semantic retrieval systems or vector databases for automotive data

Benefits

A competitive compensation package that includes a bonus component and stock options; 100% paid medical, dental, and vision premiums for full-time employees; 401K plan with a 6% employer match; Flexibility in schedule and generous paid vacation (available immediately after start date); Company-wide holiday office closures; AD+D and Life Insurance