(US) Surgical Video Annotation Program Lead (Remote) at Codvo.ai

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

28 May, 26

Salary

0.0

Posted On

27 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Program Delivery, Ontology Execution, Automation First, Quality Assurance, IRR Reporting, Adjudication Governance, Dataset Release Management, Security Compliance, Client Facing, Team Leadership, SOP Development, Metric Tracking, Video Annotation, Temporal Segmentation, Risk Management, Stakeholder Communication

Industry

Software Development

Description

Surgical Video Annotation Program Lead Job Description: Surgical Video Annotation Program Lead (Team Lead) Location: USA (Remote/Hybrid) Type: Full-time Reports to: Head of Delivery / Program Director Role goal: Own end-to-end delivery of a high-throughput, clinically defensible, audit-ready surgical video annotation program—driving automation-first workflows, quality (IRR/QA), and on-time dataset releases. What you will own Program delivery (E2E): Stand up and run the annotation “factory” from intake → de-ID → task orchestration → annotation → QA/IRR → adjudication → dataset release + evidence pack. Ontology + guidelines execution: Partner with clinical SMEs to operationalize a procedure-specific ontology (phases/steps, tools, anatomy, events) and convert it into clear labeling guidelines and UI rules. Automation-first operations: Drive pre-labeling + verification workflows (not manual-from-scratch), implement routing based on model confidence/uncertainty, and continuously reduce human effort per labeled minute. Quality system: Implement and enforce: Multi-rater sampling strategy IRR reporting by label type (kappa/alpha; IoU/Dice where applicable) Calibration loops and retraining for annotators QA gates + sampling plans with acceptance thresholds Adjudication governance: Run the disagreement workflow, manage escalation to senior annotators/clinical reviewers, track ambiguity categories, and ensure guideline updates close recurring issues. Dataset release management: Own versioning, provenance, and release discipline—ensuring every dataset is reproducible and ships with an audit-ready Evidence Pack (provenance, QA, IRR, adjudication trail, sign-offs). Security + compliance coordination: Ensure labeling operations follow enterprise security requirements (access control, logging, retention, de-identification review) and support audits/vendor risk requests. Client-facing cadence: Lead weekly operating reviews, present throughput/quality metrics, manage scope changes, and ensure PoCs convert into scaled programs. What you will build and run Team: L1 annotators, L2 senior annotators, QA auditors, adjudicators; coordinate with clinical reviewers and ML/data engineering. Operating system: SOPs, training curriculum, calibration playbooks, quality scorecards, escalation paths, and release checklists. Metrics: Throughput, cycle time, rework rate, IRR trends, defect density, acceptance pass rate, cost per labeled hour/minute, and automation leverage (pre-label acceptance rate). Required qualifications 6–10+ years in annotation operations / data operations / QA-led delivery, with at least 2+ years in a lead role managing teams and SLAs. Hands-on experience with video annotation (temporal segmentation + event labeling) and familiarity with bounding boxes/segmentation concepts. Demonstrated ability to implement multi-rater workflows, compute/interpret IRR, and run calibration to improve consistency. Strong program management skills: planning, staffing, throughput modeling, risk management, and stakeholder communication. Comfort working with tooling/APIs and structured data exports; ability to translate guidelines into tool-enforceable rules. Experience in regulated or sensitive-data environments (healthcare preferred): privacy-first mindset, audit trails, process discipline. Preferred qualifications (strong plus) Healthcare domain familiarity: surgical workflows, OR video sources (endoscopy/robotic), common quality issues (smoke, blur, blood occlusion). Experience coordinating de-identification workflows for video/audio and supporting enterprise security reviews (SOC2/ISO-type controls). Exposure to automation/ML-assisted labeling: pre-labeling, confidence routing, active learning basics. Prior work on dataset versioning and “release” discipline (e.g., DVC-like thinking, evidence packs, reproducible builds).

Responsibilities

The role owns the end-to-end delivery of a high-throughput, clinically defensible surgical video annotation program, focusing on driving automation-first workflows, quality assurance, and timely dataset releases. This includes standing up the annotation factory, operationalizing clinical ontologies, implementing quality systems, and managing dataset release discipline.