Remote Ground Truth QA Evaluator & Rubric Specialist

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Part time

Job summary

AuraOne seeks a Ground Truth QA Data Specialist to evaluate ground truth QA data prompts and responses against our quality rubric. You will compare model outputs, label edge cases, and draft structured feedback for the modeling team to retrain.

Responsibilities include evaluating outputs against a versioned rubric, tagging edge cases and unsafe content, calibrating reviewer scores against gold standards, and providing clear rationales to improve future data quality.

Qualifications

• Prior evaluation, annotation, or human-rater experience on ground truth QA data or adjacent 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 prompt issues.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate ground truth QA data evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a written rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them to the program team for rubric updates.
• Maintain reviewer-quality scores by calibration against gold-standard examples weekly.

Skills

Evaluation experience

Annotation experience

Inter-rater calibration

Attention to detail

Async availability

Job description

AuraOne seeks a Ground Truth QA Data Specialist to evaluate ground truth QA data prompts and responses against our quality rubric. You will compare model outputs, label edge cases, and draft structured feedback for the modeling team to retrain.

Responsibilities include evaluating outputs against a versioned rubric, tagging edge cases and unsafe content, calibrating reviewer scores against gold standards, and providing clear rationales to improve future data quality.