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Torc Robotics

Autonomous truck software for Level 4

Senior Machine Learning Engineer - Auto Tagging

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or a related technical field plus competencies and technical proficiencies typically acquired through 6+ years of experience; or a Master's degree in those fields plus 3+ years of experience; or a PhD in those fields plus 1+ years of experience.
  • Strong Python and SQL skills, with substantial experience processing massive time-series or unstructured datasets.
  • Hands-on machine learning and dataset curation experience, including 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 such as Ray, Spark, or Beam and cloud platforms such as AWS, GCP, or Azure for heavy data workloads.
  • Experience parsing complex data formats and applying scenario-description standards such as Pegasus layers.
  • 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.

Responsibilities

  • Architect and optimize distributed data pipelines that process massive multi-sensor logs, including camera, LiDAR, radar, and kinematics, to extract and catalog safety-critical and long-tail driving events.
  • Develop and tune heuristic-based and machine-learning-assisted algorithms, including exploring Vision-Language Models or semantic vector search, to classify and describe complex environmental and behavioral scenarios.
  • Extract and format scenario data using the Pegasus layer standard and open-source frameworks to ensure semantic consistency and rigorous metadata integrity.
  • Manage ingestion of tagged events into the observations database to enable high-speed querying and retrieval for machine-learning training, regression testing, and system validation.
  • Drive consensus across organizational boundaries and collaborate with downstream consumers in perception, simulation, and systems engineering to define interesting scenarios and operationalize a continuous data loop.
  • Guide and mentor less-experienced engineers, lead design reviews, establish coding standards, and foster technical excellence and collaborative problem-solving.

Desired Qualifications

  • 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 build a robust semantic inference engine beyond standard bounding-box object detection.
  • Familiarity with parsing robotics formats such as ROS bags and MCAP, and optimizing high-performance columnar storage formats such as Parquet and Arrow.
  • Knowledge of how scenario data feeds generative simulation workflows, neural rendering, or sensor fusion validation.
  • Experience building semantic retrieval systems or vector databases for automotive data.

About the Company

Torc Robotics

Autonomous truck software for Level 4

Torc Robotics builds software for autonomous trucks, aiming to enable Level 4 self-driving capabilities for freight transport. They develop driving systems that let heavy trucks operate autonomously under certain conditions, and they work with truck manufacturers and fleet operators to bring these solutions to market. The company integrates sensors from partners (such as Luminar Technologies) and provides ongoing updates and support to ensure reliability and performance. Unlike more general AV players, Torc focuses on commercial trucking and leverages strategic partnerships (for example with Daimler Trucks) to integrate its software into production vehicles and fleets. Its goal is to improve road safety and freight efficiency by deploying scalable, commercial autonomous trucking solutions.

Company size
501-1,000
Company stage
M&A
Total funding
Not listed
Headquarters
Blacksburg, VA
Founded
2005

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