Human Data Program Reviewer

AI Trainer Jobs; United States; Remote; USD 34,000 - 55,000; Part time

Benefits offered by this job

Remote work

Contractor role

Flexible hours

Job summary

AuraOne is seeking a remote Human Data Program Reviewer to evaluate prompts and model outputs using a versioned rubric. You will compare paired responses, label edge cases, and provide structured feedback to retrain the modeling team.

This independent contractor role is remote, with hourly compensation confirmed after the interview. Eligible from the United States, you will calibrate evaluators, write rationales, and help generate auditable labels, regression cases, and notices for rubric

Qualifications

• Prior evaluation, annotation, or human-rater experience on human data program evaluation or adjacent content for Human Data Program Reviewer 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.

Responsibilities

• Evaluate human data program evaluation model outputs against a versioned rubric and assign severity tags for Human Data Program Reviewer 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

Severity tagging

Inter-rater calibration

Human Data Program evaluation

Error spotting

Rationale writing

Cross-cutting evaluation

AI evaluation

Job description

About the role

Human Data Program Reviewer is a remote evaluation track for reviewing human data program 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 human data program 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 human data program evaluation model outputs against a versioned rubric and assign severity tags for Human Data Program Reviewer 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.

Role details

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 human data program evaluation or adjacent content for Human Data Program Reviewer 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 human data program 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
• Human Data Program evaluation
• Cross-cutting evaluation roles
• AI evaluation
• Rubric writing
• Expert review

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow-up.
  Placement timing depends on program demand and reviewer confirmation.