$177,300 – $212,800
Listed on Torc Robotics’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads the development of large-scale data pipelines and machine learning systems that extract and categorize safety-critical events from autonomous vehicle sensor logs, feeding a curated library that accelerates perception and simulation across the company. You'll architect distributed systems for petabyte-scale multi-modal data processing, mentor junior engineers, and collaborate cross-functionally to define what scenarios matter most for safe autonomous trucking.
Our summary, not Torc Robotics’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- Apache Beam
- Apache Spark
- Databricks
- Machine Learning
- Microsoft Azure
- Python
- Robot Operating System (ROS)
- SQL
- V-Ray
- Vector Databases
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What they ask for
Required
- BS or MS in Computer Science, Robotics, Engineering, or STEM field
- 6+ years in data engineering, ML systems, or autonomous data curation
- Strong Python and SQL skills with experience on massive time-series or unstructured datasets
- Hands-on machine learning and dataset curation with track record of improving downstream model performance
- Experience with Databricks or similar platforms for large-scale analytics
- Expertise in distributed compute frameworks like Spark, Ray, or Beam
- Experience with AWS, GCP, or Azure for heavy data workloads
- Experience parsing complex data formats and applying Pegasus layer standards
- Exceptional communication and ability to translate data engineering challenges for cross-functional stakeholders
- Proven track record of mentoring teams and driving system architecture
Nice to have
- Familiarity with foundational models, auto-labeling pipelines, or zero-shot classification
- Experience with vLLM, SGLang, or similar high-throughput model serving frameworks
- Experience with semantic extraction and attribute mapping for semantic inference engines
- Familiarity with parsing robotics formats like ROS bags and MCAP
- Knowledge of how scenario data feeds into generative simulation, neural rendering, or sensor fusion validation
- Experience building semantic retrieval systems or vector databases for automotive data