Expert Data Quality Reviewer

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

Job summary

AuraOne is seeking an Expert Data Quality Reviewer to remotely evaluate data prompts and responses per a versioned rubric. You will compare outputs, label edge cases, and provide structured feedback for retraining.

Role involves designing and auditing label taxonomies, helping turn evaluation results into auditable labels and regression cases for AuraOne Human Data. Strong writing and attention to detail are essential.

Qualifications

• Experience reviewing data quality evaluation prompts and outputs.
• Ability to apply rubrics consistently across long batches.
• Clear, written rationale naming issues and rubric clauses.
• High attention to detail and ability to flag prompts with issues.
• Reliable asynchronous availability for at least 10 hours per week.

Responsibilities

• Evaluate data quality outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger one with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route to program team for rubric updates.
• Calibrate reviewer quality against gold-standard examples weekly.

Skills

Prior evaluation experience

Rubric application

Written reasoning

Attention to detail

Async availability 10h/w

Job description

About the role

Expert Data Quality Reviewer is a remote evaluation track for reviewing data quality 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 data quality evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Design and audit label taxonomies, knowledge graphs, and evaluation datasets.

Responsibilities

• Evaluate data quality evaluation model outputs against a versioned rubric and assign severity tags for Expert Data Quality 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 data quality evaluation or adjacent content for Expert Data Quality 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.

Example tasks

• Compare two data quality 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
• Data Quality 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.