Video Data Annotator

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

Job summary

AuraOne is seeking a Video Data Annotator for a remote evaluation track to review prompts and responses against our quality rubric. Reviewers compare outputs, label edge cases, and write structured feedback to retrain the model.

You will assess video data outputs, judge spatial reasoning, and help maintain calibration scores. This contractor role requires attention to detail, prior annotation experience, and reliable async availability for at least 10 hours per week.

Qualifications

• Prior evaluation or annotation experience on video data or adjacent content.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause.
• Reliable async availability for at least 10 hours per week.
• Strong attention to detail and ability to flag prompt problems.

Responsibilities

• Evaluate video data annotator outputs against a versioned rubric.
• Assign severity tags for assignments and justify with rationale.
• Label hallucinations, instruction-following failures, and unsafe content.
• Capture ambiguous prompts and route to program team for rubric updates.
• Calibrate reviewer quality against gold-standard examples weekly.

Skills

Evaluation experience

Annotation experience

Inter-rater calibration

Attention to detail

Written reasoning

Job description

Video Data Annotator is a remote evaluation track for reviewing video data annotator 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

Video Data Annotator is a remote evaluation track for reviewing video data annotator 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.

Role details

About the role

Video Data Annotator is a remote evaluation track for reviewing video data annotator 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 video data annotator 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 video data annotator evaluation model outputs against a versioned rubric and assign severity tags for Video Data Annotator 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on video data annotator evaluation or adjacent content for Video Data Annotator 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 video data annotator 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
• Video Data Annotator evaluation
• Design