The role involves designing workflows for robotics datasets, owning contributor scoring, inspection, AutoQA thresholds, and acceptance logic to ensure quality and efficiency in robotics data labeling. The position requires building contributor onboarding workflows, defining review levels, identifying failure modes, translating failure cases into rules, automating validation, optimizing review load, and developing contributor confidence systems. The role is critical in scaling robotics projects by improving throughput, reducing costs, and ensuring high-quality data for AI models, directly impacting Labelbox's robotics business and AI development efforts.