Video Editing Professional

AuraOne, Inc.; United States; Remote; USD 57,000 - 81,000; Full time

Job summary

AuraOne, Inc. is seeking a remote Video Editing Professional contractor to review evaluation prompts and responses against our quality rubric. You will compare paired outputs, assign severity tags, and provide structured feedback for retraining.

This role supports the AI data labeling team; requires attention to detail, ability to articulate rationales, and reliable weekly availability for at least 10 hours.

Qualifications

• Prior evaluation, annotation, or human-rater experience on video editing evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate video editing evaluation model outputs against a versioned rubric and assign severity tags for Video Editing Professional 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.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Video Editing evaluation

Job description

Video Editing Professional is a remote evaluation track for reviewing video editing 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.

Why this role matters

AI data reviewers help turn video editing evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.

Responsibilities

• Evaluate video editing evaluation model outputs against a versioned rubric and assign severity tags for Video Editing Professional 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.
• Document recurring failure modes so the modeling team can target them in the next training run.

Qualifications

• Prior evaluation, annotation, or human-rater experience on video editing evaluation or adjacent content for Video Editing Professional 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 editing 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.

Nice to have

• 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.

Skills

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Video Editing evaluation

Work model

Remote — US-eligible. Remote · Independent specialist contractor. Employment type: CONTRACTOR. Applicants must be authorized to work from US.

Compensation

$50 / hr

Prior evaluation, annotation, or human-rater experience on video editing evaluation or adjacent content for Video Editing Professional 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.