Data Operations Engineer
On-siteSan Francisco, California, United States
Job Summary
Own the end-to-end relationship with the data labeling provider, managing task scoping, timelines, and issue resolution. Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines, and dataset browsers. Define labeling taxonomies, annotation specifications, and quality control standards while implementing automated checks and audit workflows. Partner with researchers to translate perception model needs into data collection strategies, identifying coverage gaps across object types, scenes, lighting conditions, and sensor modalities. Build dashboards to monitor dataset diversity, class balance, and domain coverage. Close the loop on the data flywheel by tracking labeled data flow into training, surfacing failure modes, and driving pipeline iteration from collection through model improvement. Evaluate and integrate new data sources.
Required 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
- Highly organized, with a track record of managing multiple concurrent workstreams
- Self-directed and autonomous
Desired Qualifications
- Bonus: experience with computer vision data, annotation platforms, or labeling operations
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