Torc Robotics is a leader in autonomous driving technology, focused on developing software for automated trucks. They are seeking a Senior Autonomy Software Systems Engineer who will build data pipelines and tools for analyzing large-scale autonomy datasets, collaborating with various teams to enhance data-driven decision-making.
Responsibilities:
- Build and maintain Python-based data pipelines to ingest, process, and analyze large-scale autonomy datasets
- Develop tools to extract and manipulate data from cloud-based storage systems (AWS, GCP, etc.)
- Work with HD/SD map data, vehicle telemetry, and simulation outputs to support autonomy workflows
- Write performant and scalable code for data processing, transformation, and analysis
- Collaborate with systems, mapping, and AI/ML teams to support data-driven decision making
- Develop scripts and tools to support scenario generation, ODD analysis, and validation workflows
- Optimize data workflows for performance, scalability, and reliability
- Contribute to system-level debugging by analyzing data and identifying root causes of issues
- Support integration of data pipelines into broader autonomy and validation systems
Requirements:
- BS + 6+ years, MS + 3+ years, or PhD + 1+ years in Computer Science, Software Engineering, Robotics, or related field
- Strong proficiency in Python for production-level development
- Experience working with large-scale datasets and data processing systems
- Experience with cloud environments and distributed data systems (AWS, GCP, Azure)
- Familiarity with SD/HD maps, geospatial data, or spatial data processing
- Experience writing clean, maintainable, and scalable code
- Strong debugging and problem-solving skills in complex systems
- Experience working in cross-functional engineering environments
- Experience with C++ in production systems
- Experience working with autonomy, robotics, or mapping systems
- Experience with data pipelines, ETL systems, or ML data workflows
- Exposure to ROS/ROS2 or similar robotics frameworks
- Experience with containerization (Docker) and distributed compute systems