Low-Resource Language AI Evaluator

AI Trainer Jobs; United States; Remote; USD 28,000 - 41,000; Part time

Job summary

AuraOne is seeking a Remote Low-Resource Language AI Evaluator to review prompts and responses against our quality rubric. You will compare paired outputs, label edge cases, and write structured feedback to help retrain models.

Responsibilities include evaluating model outputs to rubric, selecting stronger answers with rationale, tagging policy and accuracy issues, and calibrating scores against gold-standard examples weekly.

Qualifications

• Prior evaluation, annotation, or human-rater experience on low resource language AI evaluation or adjacent content.

Responsibilities

• Evaluate 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 back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Skills

Evaluation experience

Annotation/labeling

Attention to detail

Written reasoning

Async availability

Tools

Rubric-based annotation

Job description

Low-Resource Language AI Evaluator is a remote evaluation track for reviewing low resource language ai 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.

Category: Frontier Model Evaluation · Pay: Hourly rate confirmed after the interview process · Location: Remote — US-eligible · Contractor

About the role

Low-Resource Language AI Evaluator is a remote evaluation track for reviewing low resource language ai 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 low resource language ai 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 low resource language ai evaluation model outputs against a versioned rubric and assign severity tags for Low-Resource Language AI Evaluator 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 low resource language ai evaluation or adjacent content for Low-Resource Language AI Evaluator 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 low resource language ai 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
• Low Resource Language AI evaluation
• Localization review
• Cultural context
• Language evaluation
• Resource
• Language