Data Annotator - Video Content

AI Trainer Jobs; United States; Remote; USD 21,000 - 34,000; Part time

Job summary

AI Trainer Jobs is seeking a Data Annotator - Video Content for a remote evaluation track to assess prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and provide structured feedback for retraining.

Responsibilities include evaluating model outputs against rubric, tagging content issues, maintaining calibration against gold standards, and routing ambiguous prompts to the program team for rubric updates.

Responsibilities

• Evaluate data annotator video content evaluation outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route to program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Job description

Data Annotator - Video Content is a remote evaluation track for reviewing data annotator video content evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.

Category: Video, Vision & Spatial AI · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

Data Annotator - Video Content is a remote evaluation track for reviewing data annotator video content evaluation prompts and responses against AuraOne's quality rubric.

About the role

Data Annotator - Video Content is a remote evaluation track for reviewing data annotator video content evaluation prompts and responses against AuraOne's quality rubric. Reviewers compare paired outputs, label edge cases, and write the kind of structured feedback the modeling team can use to retrain.
AI data reviewers help turn data annotator video content evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

Responsibilities

• Evaluate data annotator video content evaluation model outputs against a versioned rubric and assign severity tags for Data Annotator - Video Content assignments.
• Compare paired responses and pick the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Role details

Track Evaluation & annotation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Prior evaluation, annotation, or human-rater experience on data annotator video content evaluation or adjacent content for Data Annotator - Video Content work.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Reliable async availability for at least 10 hours per week.

Role signals

Example tasks

• Compare two data annotator video content evaluation model responses to the same prompt and pick the stronger one with rationale.
• Tag an unsafe response with the correct policy category and severity.
• Audit a 50-row batch for rubric consistency and report drift to the program lead.
• Propose a rubric clarification after spotting a recurring failure mode.

Useful experience

• Background in linguistics, content moderation, or trust & safety review.
• Experience with inter-rater agreement metrics and calibration cycles.
• Domain expertise that lets you spot subject-matter errors automated checks miss.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Data Annotator Video Content evaluation
• Design