Remote Indigenous Language Context Evaluator

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

Job summary

AuraOne, Inc. is seeking an Indigenous Language Cultural Context Reviewer to work remotely as an independent contractor in the United States. You will review prompts and model outputs, apply a versioned rubric, and assign severity tags.

You will compare paired responses, write clear rationales, and contribute to retraining data by identifying edge cases and documenting failure modes for the modeling team.

Qualifications

• Prior evaluation, annotation, or human-rater experience on indigenous language cultural context evaluation or adjacent content for Indigenous Language Cultural Context Reviewer work.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Reliability in asynchronous work and meeting weekly calibration tasks.

Responsibilities

• Evaluate indigenous language cultural context evaluation model outputs against a versioned rubric and assign severity tags for Indigenous Language Cultural Context 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.
• 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

Indigenous Language Cultural Context

Localization review

Cultural context

Language evaluation

Indigenous

Language

Job description

AuraOne, Inc. is seeking an Indigenous Language Cultural Context Reviewer to work remotely as an independent contractor in the United States. You will review prompts and model outputs, apply a versioned rubric, and assign severity tags.

You will compare paired responses, write clear rationales, and contribute to retraining data by identifying edge cases and documenting failure modes for the modeling team.