Senior, ML Engineer - Auto Tagging

Job description

Job 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…
• 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…
• 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…
• 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.

Job 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.