AI Data Annotation Expert

AI Trainer Jobs; United States; Remote; USD 34,000 - 62,000; Part time

Job summary

AuraOne is seeking an AI Data Annotation Expert for a remote evaluation track. You will review prompts and responses against a versioned rubric, provide structured feedback, and help turn outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.

You will compare paired outputs and tag issues to support model improvement. Applicants should have prior evaluation or annotation experience, the ability to apply rubrics consistently, and strong written reasoning with

Qualifications

• Prior evaluation, annotation, or human-rater experience on ai data annotation evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate ai data annotation evaluation model outputs against a versioned rubric and assign severity tags for AI Data 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.

Skills

Model output evaluation

Rubric-based annotation

Inter-rater calibration

Attention to detail

Job description

AI Data Annotation Expert is a remote evaluation track for reviewing ai data 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: Frontier Model Evaluation · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

AI Data Annotation Expert is a remote evaluation track for reviewing ai data annotation evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

AI Data Annotation Expert is a remote evaluation track for reviewing ai data annotation evaluation prompts and responses against AuraOne's quality rubric.
AI data reviewers help turn ai data annotation evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review frontier model outputs. Judge benchmark failures and calibrate other evaluators.

Responsibilities

• Evaluate ai data annotation evaluation model outputs against a versioned rubric and assign severity tags for AI Data 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 ai data annotation evaluation or adjacent content for AI Data 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 ai data 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
• AI Data Annotation evaluation
• AI model evaluation