Madurese Language Expert

AI Trainer Jobs; United States; Remote; USD 34,000 - 62,000; Part time

Job summary

AI Trainer Jobs seeks a Madurese Language Expert for a remote evaluation track. You will review prompts and model outputs, apply a quality rubric, and generate structured feedback to retrain systems.

The role involves comparing responses, labeling issues, and contributing to calibration with weekly tasks. The position is an independent contractor with hourly pay to be confirmed after interview, and is US-eligible with remote work options.

Qualifications

• Experience evaluating language prompts and model outputs.
• Ability to apply rubrics consistently across long batches.
• Clear written reasoning naming issues and rubric clauses.

Responsibilities

• Evaluate Madurese language outputs against a rubric and assign severity tags.
• Compare paired responses and select the stronger one with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and refer them for rubric updates.
• Maintain reviewer quality by calibration and tracking against gold standards.

Skills

Madurese language evaluation

Annotation

Human rater experience

Job description

Madurese Language Expert is a remote evaluation track for reviewing madurese 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

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

Role details

About the role

Madurese Language Expert is a remote evaluation track for reviewing madurese 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 madurese 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 madurese language evaluation model outputs against a versioned rubric and assign severity tags for Madurese 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.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 madurese language evaluation or adjacent content for Madurese 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.

Role signals

Example tasks

• Compare two madurese 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
• Madurese Language evaluation