Data Operations Manager

Responsibilities

• Convert perception and robot-learning requirements into data collection plans, annotation ontologies, instructions, examples, and acceptance criteria.
• Coordinate external agencies and in-house robot testing teams for curated data collection.
• Build, train, and manage internal annotators and external vendors; track throughput, quality, and cost.
• Design sampling, review, adjudication, gold-set, inter-annotator agreement, and audit processes.
• Maintain dataset versions, metadata, provenance, consent and usage records, and engineering handoffs.
• Partner with ML engineers to diagnose model failures and prioritize high-value data.
• Build dashboards for coverage, quality, turnaround time, backlog, rework, and unit economics.
• Protect sensitive customer and site data through access, retention, and handling processes.

Requirements

• 2+ years in data operations, annotation, data collection, ML operations, quality operations, or related program management.
• 2+ years managing external agencies and stakeholders.
• Experience leading people or vendors and delivering large, quality-controlled datasets on schedule.
• Experience mentoring and training junior staff.
• Ability to write precise annotation guidelines and translate ambiguous technical needs into repeatable workflows.
• Comfort with spreadsheets, dashboards, issue trackers, and data-quality metrics.
• Strong analytical judgment, attention to detail, and cross-functional communication.
• Bachelor’s degree or equivalent practical experience in engineering, data, operations, or a related field.

Nice-to-haves

• Experience with computer vision, robotics, autonomous vehicles, mapping, speech, multimodal data, or sensor-rich ML products.
• Experience with teleoperation or embodied-AI data collection, video annotation, 3D point clouds, segmentation, tracking, or key-point labeling.
• Familiarity with Python, SQL, annotation platforms, privacy controls, and vendor contracting.