Video Annotation Expert

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Part time

Job summary

AI Trainer Jobs seeks a Video Annotation Expert for a remote evaluation track. You will review prompts and responses against AuraOne's quality rubric, label edge cases, and provide structured feedback to support model retraining.

Reviewers compare paired outputs, judge spatial reasoning, and maintain calibration scores as part of a flexible contractor role with hourly compensation to be confirmed after interview.

Qualifications

• Experience evaluating video annotation outputs using multi-page rubrics.
• Ability to apply rubric clauses consistently across batches.
• Clear written reasoning naming issues and rubric clauses.

Responsibilities

• Evaluate model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them for rubric updates.
• Maintain reviewer-quality scores by calibration against gold-standard examples weekly.

Skills

Annotation evaluation

Rubric-based reasoning

Attention to detail

Asynchronous availability

Written reasoning clarity

Job description

Video Annotation Expert is a remote evaluation track for reviewing video annotation 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 Annotation Expert is a remote evaluation track for reviewing video annotation evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

Video Annotation Expert is a remote evaluation track for reviewing video annotation 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 annotation 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 annotation evaluation model outputs against a versioned rubric and assign severity tags for Video Annotation Expert 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

What you should bring

• Prior evaluation, annotation, or human-rater experience on video annotation evaluation or adjacent content for Video Annotation Expert 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 video annotation 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 Annotation evaluation
• Design