Senior, Software Engineer - ML Data Delivery

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

The Pseudo-Labeling team's goal is to create high-quality annotations on sensor data (images, point clouds). The annotations include 2D, 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, etc. The annotations are then used by different downstream users — for example, perception teams use them to train various models, and simulation teams use them for generating new data. This role sits at the intersection of that pseudo-labeling data and the online perception team, building the systems that select, package, and deliver the right data to feed on-demand model training.

• →Design, implement, test and deploy tooling and pipelines for internal quality control and issue identification of pseudo-labeled data, ensuring annotations meet the bar required by downstream model training.
• →Provide statistical support and report on pseudo-label quality, coverage, and pipeline health to internal stakeholders and leadership.
• →Support secondary data selection (virtual packaging) to curate and feed on-demand data to online model training.
• →Support the build-out of the ML data delivery system that enables the online perception team to train models on-demand.
• →Drive general pipeline improvement and optimization across the pseudo-labeling and data delivery stack, identifying and resolving bottlenecks in throughput, quality, or reliability.
• →Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution.
• →Stay up to date with the latest developments in offline perception, data pipeline engineering, and ML data infrastructure for autonomous driving.
• →Independently develop tools, services, and algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.
• →Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics and ML training workflows.
• →Proactively assess current capabilities to identify areas for improvement, proposing solutions that align with core strategy and operation.
• →Guide and produce information products, supporting visualization and data accessibility in a customer-centric manner.
• →Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.
• →Develop guidelines and standards for data quality control, data delivery systems, and their deployment, and associated processes.
• →Provide technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.

• Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.
• Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains and owns team technical solutions and drives consensus. Mentors and guides engineers within the group.
• 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;
• Required Qualifications (some combination of the following skills):
  • Familiarity with the offline perception stack in general, and knowledge of how pseudo-label data is produced and related best practices.
  • Strong software engineering background building and operating data pipelines and services at scale.
  • Statistical analysis and reporting skills, with the ability to translate data quality findings into actionable insights.
  • Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLflow, Weights and Biases, ML Metrics and Evaluation / Quality.
  • Model Data Curation – Parquet data processing (PyArrow, Daft, Pandas, etc).
  • Development Tools & Eco-System (at scale) – Proficiency in Python software development. Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.
  • Experience with distributed data processing and/or ML frameworks – PyTorch, Lightning, Ray, or similar.

• Experience with large-scale data delivery systems and associated quality control.
• Pseudo-labeling experience in general.