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Codvo AI · IT - Software Development

Surgical Video Data & Annotation Operations Manager

India, Pune · Job Posted June 03, 2026
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Job Description

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.

Job Responsibility

  • Program Ownership: Deliver ≥10,000 labeled video hours/month within SLA, maintain ≥70% auto-accept rate via model-assisted labeling
  • Ontology & Guidelines: Define and version surgical ontologies, author annotation guidelines, run change control
  • Tooling & Automation: Stand up and operate CVAT, integrate MONAI Label, use FiftyOne for QA, implement propagation and confidence-based routing, partner with ML to deploy pre-label models
  • Quality, IAA & Release Gates: Define gold sets and acceptance thresholds, run double-labeling, adjudication, and weekly QA reviews

Requirements

  • Program Ownership & Throughput: Deliver ≥10,000 labeled video hours/month within SLA (≤7 days ingest→approved), Maintain ≥70% auto-accept rate via model-assisted labeling, drive to 80%+ over time, Operate daily pipeline: ingest → de-id → pre-labels → annotation → QA → release, Manage backlog, staffing, and shift planning to sustain 333+ hours/day throughput
  • Ontology & Guidelines: Define and version surgical ontologies: phases, steps, events, tools, anatomy, quality flags, Author annotation guidelines with boundary rules and ambiguity handling, Run change control and backward compatibility across dataset versions
  • Tooling & Automation: Stand up and operate CVAT (or equivalent) with API-driven workflows, Integrate MONAI Label (or similar) for model-assisted segmentation/active learning, Use FiftyOne (or equivalent) for dataset QA, error analysis, and sampling, Implement propagation (tracking/interpolation) and confidence-based routing, Partner with ML to deploy pre-label models (tool detection, phase recognition, SAM-style masks)
  • Quality, IAA & Release Gates: Define gold sets and acceptance thresholds: Phase/event F1 ≥ 0.92, Tool mAP ≥ 0.85, Anatomy Dice ≥ 0.85, Run double-labeling (≥10%), adjudication, and weekly QA reviews
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