Bilingual Spanish AI Data Evaluator - Remote

AuraOne; United States; Remote; USD 28,000 - 41,000; Full time

Job summary

AuraOne is seeking a remote, contract-based bilingual Benefits Counselor (Spanish) to review and label model outputs against a quality rubric, providing structured feedback to improve AI data quality. This is a remote evaluation track with US eligibility and an hourly pay arrangement to be confirmed after interview.

You will compare paired responses, name issues with rubric references, tag unsafe or hallucinations, and document recurring failures for the training team.

Qualifications

• Prior evaluation/annotation/human-rater experience on Spanish generalist evaluation or adjacent content for Bilingual (Spanish) Benefits Counselor work.
• Experience applying multi-page rubrics consistently across long batches.
• Clear written reasoning naming the issue and rubric clause being applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate Spanish generalist evaluation model outputs against a versioned rubric and assign severity tags for Bilingual (Spanish) Benefits Counselor 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.
• 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

Spanish generalist evaluation

Job description

AuraOne is seeking a remote, contract-based bilingual Benefits Counselor (Spanish) to review and label model outputs against a quality rubric, providing structured feedback to improve AI data quality. This is a remote evaluation track with US eligibility and an hourly pay arrangement to be confirmed after interview.

You will compare paired responses, name issues with rubric references, tag unsafe or hallucinations, and document recurring failures for the training team.