Senior, ML Engineer - Auto Tagging

Meet The Team

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.

What You'll Do

• 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.

What You'll Need to Succeed

• 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 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.

Bonus Points!

• 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.

Perks of Being a Torc'r

• Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future.
• Our culture is collaborative, energetic, and team focused.
• Torc offers:

• 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

At Torc, we're committed to building a diverse and inclusive workplace. Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.

Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.

• Job ID: R-102717
• Hiring Range for Job Opening
• US Pay Range $177,300—$212,800 USD