Wolof Language Expert

AI Trainer Jobs; United States; Remote; USD 28,000 - 48,000; Part time

Job summary

AI Trainer Jobs is seeking a Wolof Language Expert for remote evaluation work reviewing wolof prompts and responses against AuraOne's rubric. You will compare outputs, assign severity, and provide structured feedback to help retrain the model.

This contractor role requires attention to detail, ability to follow a multi-page rubric, and reliable availability for at least 10 hours per week; compensation is hourly and determined after interview.

Qualifications

• Experience in wolof language evaluation or related review.
• Ability to apply rubrics consistently.
• Clear written reasoning naming the issue and rubric clause.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate wolof language evaluation 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.

Skills

Wolof language

Rubric annotation

Severity tagging

Inter-rater calibration

Model evaluation

Job description

Wolof Language Expert is a remote evaluation track for reviewing wolof language 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

Wolof Language Expert is a remote evaluation track for reviewing wolof language evaluation prompts and responses against AuraOne's quality rubric.

About the role

Wolof Language Expert is a remote evaluation track for reviewing wolof language 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 wolof language 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 wolof language evaluation model outputs against a versioned rubric and assign severity tags for Wolof Language Expert 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on wolof language evaluation or adjacent content for Wolof Language Expert 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 wolof language 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
• Wolof Language evaluation