Remote ERP AI Evaluation & Quality Reviewer

AuraOne, Inc.; United States; Remote; USD 34,000 - 55,000; Full time

Job summary

AuraOne, Inc. seeks remote AI data reviewers to evaluate ERP workflow AI outputs. You will compare paired responses, label edge cases, and provide structured feedback for retraining the model. Remote, contractor role with flexible weekly hours and US eligibility.

You will maintain quality scores by calibration against gold-standard examples, document recurring failure modes, and route ambiguous prompts for rubric updates. Strong attention to detail and clear reasoning are essential.

Qualifications

• Experience evaluating ERP workflow AI evaluation or similar content.
• Comfort applying multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and the rubric clause applied.
• Strong attention to detail and ability to flag when a prompt is the problem.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate ERP workflow AI evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and pick the stronger answer with 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.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

ERP Workflow evaluation

Workflow evaluation

Tool-use review

Operational judgment

Job description

AuraOne, Inc. seeks remote AI data reviewers to evaluate ERP workflow AI outputs. You will compare paired responses, label edge cases, and provide structured feedback for retraining the model. Remote, contractor role with flexible weekly hours and US eligibility.

You will maintain quality scores by calibration against gold-standard examples, document recurring failure modes, and route ambiguous prompts for rubric updates. Strong attention to detail and clear reasoning are essential.