Data Annotator - Video Content

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

Data Annotator
- Video Content is a remote evaluation track for reviewing data annotator video content evaluation prompts and responses against Aura One'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 Aura One Human Data.
Review image and video output. Judge spatial reasoning and scene understanding.

• 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.

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

• 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.

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.

• 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.

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.

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