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# Quality Specialist - Data Annotation

- **Company:** Alaska Cover-all, LLC
- **Location:** United States
- **Experience:** Experienced
- **Salary:** $45,000.0 - $65,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, DataOps
- **Published:** September 3, 2026
- **Apply:** https://midohiovalleyjobs.com/job_view.php?bjobid=36248579130&keyword=%C2%AEion_browse=&category=999

## About the Role

* Experience: 2+ years in data annotation and quality assurance, including reviewing and calibrating other annotators' work.
 * Technical Tooling: Experience with Encord or equivalent enterprise annotation platforms (e.g., CVAT, Labelbox, SuperAnnotate); comfort with complex 2D/3D annotation (bounding boxes, polygons, masks).
 * Judgment & Attention to Detail: Exceptional ability to spot, track, and adjudicate out-of-stock products and edge cases across large video datasets - and to define a defensible standard for them.
 * Communication: Strong verbal and written skills to document standards clearly, justify quality decisions, and collaborate with a global, cross-functional team.

## Description

As a Quality Specialist with Augmodo, you will be a part of a small group of individuals who will collaborate across multiple teams to define our Quality goals.

This role is about setting the quality standard, not just doing production work. You'll be a trusted expert-the person we turn to for tough edge cases and the one who creates the benchmark data others are measured against. You'll work closely with our Computer Vision and Data Operations teams to help improve and define our quality pipeline.

Areas of Focus

 * Golden datasets & ground truth: Curate and maintain the critical benchmark datasets with our team, used to certify annotators, score vendors, and measure pipeline drift.
 * Certification & training: Create and update training materials and certification tests (measuring precision and speed) required for vendor and annotator onboarding.
 * Drift monitoring & scoring: Periodically evaluate annotators against benchmark data to identify drift and help manage QA metrics and scoring models for the Data Ops Hub.
 * Quality audits: Conduct rigorous, double-blind quality audits to ensure a gold standard across computer-vision datasets.
 * Production labeling: Serve as a high-trust annotator to directly handle production labeling when critical (P0) needs arise.


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