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Full Time Video Annotation information

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$26K

$59.8K

$95K

How much do full time video annotation jobs pay per year?

As of May 30, 2026, the average yearly pay for full time video annotation in the United States is $59,788.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,000.00 and $69,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Video Annotation Specialist, and why are they important?

To thrive as a Full Time Video Annotation Specialist, you need strong attention to detail, familiarity with video formats, and basic computer literacy, often supported by a high school diploma or equivalent. Experience using annotation tools like CVAT, Labelbox, or VGG Image Annotator is typically required, and knowledge of data labeling standards is a plus. Excellent time management, communication skills, and the ability to focus for extended periods make someone stand out in this position. These skills and qualities are vital for producing accurate, high-quality datasets that power machine learning and AI applications.

What are some common challenges faced in a full-time video annotation role, and how can they be addressed?

A common challenge in full-time video annotation is maintaining high accuracy and consistency while labeling large volumes of video data, which can be repetitive and mentally demanding. Annotators must stay focused to avoid errors, especially when distinguishing subtle differences between objects or actions across frames. To address these challenges, it's helpful to take regular short breaks, collaborate with teammates on ambiguous cases, and make use of detailed annotation guidelines provided by the employer. Many companies also offer feedback sessions and quality assurance checks to help annotators improve their work and learn best practices.

What is a Full Time Video Annotation job?

A Full Time Video Annotation job involves watching video footage and labeling or tagging specific objects, actions, or events within the video. This work is essential for training artificial intelligence and machine learning models, especially in areas like self-driving cars, security, and content moderation. Full-time annotators are expected to maintain high accuracy and consistency while working with large volumes of data. The job may require familiarity with specialized annotation tools, attention to detail, and sometimes, a basic understanding of the domain featured in the videos.

What is the difference between Full Time Video Annotation vs Data Labeling Specialist?

AspectFull Time Video AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; training in annotation toolsHigh school diploma or equivalent; training in labeling techniques
Work EnvironmentRemote or office-based, using annotation softwareRemote or office-based, using labeling platforms
Industry UsageAutonomous vehicles, AI training, surveillanceMachine learning, AI datasets, computer vision

Full Time Video Annotation involves detailed labeling of video data for AI training, often requiring specific software skills. Data Labeling Specialists focus on annotating various data types, including images and text. Both roles are essential in AI development, but Full Time Video Annotation emphasizes video-specific tasks and tools.

More about Full Time Video Annotation jobs
What cities are hiring for Full Time Video Annotation jobs? Cities with the most Full Time Video Annotation job openings:
What are the most commonly searched types of Video Annotation jobs? The most popular types of Video Annotation jobs are:
What states have the most Full Time Video Annotation jobs? States with the most job openings for Full Time Video Annotation jobs include:
What job categories do people searching Full Time Video Annotation jobs look for? The top searched job categories for Full Time Video Annotation jobs are:
Full Time Video Annotation jobs near you
Infographic showing various Full Time Video Annotation job openings in the United States as of May 2026, with employment types broken down into 87% Part Time, and 13% Contract. Highlights an 66% Physical, 17% Hybrid, and 17% Remote job distribution, with an average salary of $59,788 per year, or $28.7 per hour.

(US) QA Auditor (Sampling, Quality Gates, Release Readiness)

Codvo Private Limited

Manhattan, NY • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

QA Auditor (Sampling, Quality Gates, Release Readiness) - USA JD: QA Auditor — Surgical Video Annotation (Sampling, Quality Gates, Release Readiness) Location: USA | Type: Full-time Reports to: Quality Lead / Program Lead Role goal: Protect dataset quality at scale by running sampling-based audits, enforcing quality gates, and ensuring every dataset release is audit-ready and meets acceptance thresholds. About Us 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. Responsibilities Execute sampling plans (stratified by label type, difficulty, site/surgeon/device, model confidence) for ongoing QA and release audits. Audit annotations against guidelines + ontology; score defects using standardized rubrics (severity, type, root cause).

Run and validate quality gates prior to release: completeness/coverage checks, schema/constraint checks (illegal combinations, temporal consistency, boundary rules), defect density and rework thresholds. Generate rework tickets with precise instructions; track closure and verify fixes. Partner with Annotation Lead to drive calibration actions (training refreshers, guideline clarifications, examples library).

Maintain QA dashboards: defect rate, rework %, gate pass rate, audit cycle time, top error patterns by annotator/team/label type. Support release readiness: ensure required artifacts are complete for the Evidence Pack (QA results, sampling logs, sign-offs). Escalate systematic issues to Adjudication/Clinical Review (definition gaps, ambiguous classes, recurring edge cases).

Deliverables (what you produce weekly) QA audit reports (sample size, findings, severity distribution, root‐cause categories) Gate status + release checklist sign‐off recommendations Rework queue and closure verification Trend insights and corrective action plan (top 3 issues + fixes) Requirements 3–7+ years in data QA / annotation QA / operations quality; experience auditing labeled datasets (video preferred). Strong command of sampling-based QA: stratified sampling, acceptance thresholds, and audit scoring. High attention to detail; comfortable reading and enforcing detailed SOPs/guidelines.

Strong documentation skills; can write clear defect descriptions and rework instructions. Comfortable with dashboards/spreadsheets; basic metric literacy (defect density, rework, throughput). Note- Please apply via our official careers portal only, as applications sent directly to executives may not be considered.

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