Data Operations Engineer

Role

Responsibilities

• Own the end-to-end relationship with our data labeling provider, including task scoping, timeline management, and issue resolution
• Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines, and dataset browsers
• Define and enforce quality control standards across all labeled data, implementing automated checks and audit workflows
• Partner with researchers to translate perception model needs into data collection strategies, identifying gaps in coverage across object types, scenes, lighting conditions, and sensor modalities
• Build dashboards and metrics to monitor dataset diversity, class balance, and domain coverage
• Close the loop on the data flywheel: track how labeled data flows into training, surface failure modes, and drive iteration on the pipeline from collection through to model improvement
• Evaluate and integrate new data sources
• Define labeling taxonomies and annotation specifications

Qualifications

• 1-3+ years of experience in data operations, project management, or a technical coordination role, ideally supporting ML or engineering teams
• Proficiency in Python and comfort building lightweight tools, scripts, and dashboards
• Strong written and verbal communication skills, with experience managing external vendors or cross-functional stakeholders
• Familiarity with ML workflows and how training data impacts model performance