Multilingual Taxonomy Data Specialist

AI Trainer Jobs; United States; Remote; USD 110,000 - 165,000; Full time

Job summary

AuraOne seeks a Multilingual Taxonomy Data Specialist to join a remote evaluation track. You will review multilingual taxonomy prompts and responses against our quality rubric, compare paired outputs, label edge cases, and write structured feedback to retrain models.

This independent contractor role requires strong attention to detail, clear written reasoning, and at least 10 hours per week of async availability. Experience with inter-rater calibration and taxonomy design is a plus.

Qualifications

• Experience in multilingual taxonomy data evaluation or related content.
• Clear written reasoning that names the issue and the rubric clause.
• At least 10 hours per week of async availability.
• Strong attention to detail and ability to flag prompt problems.

Responsibilities

• Evaluate multilingual taxonomy data outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples weekly.

Skills

Multilingual taxonomy data

Attention to detail

Structured feedback

Async availability

Tools

Rubric-based annotation tools

Job description

Multilingual Taxonomy Data Specialist is a remote evaluation track for reviewing multilingual taxonomy data 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: Data Quality, Taxonomy & Ontology · Pay: $80–$120 / hr · Location: Remote — US-eligible · Contractor

Multilingual Taxonomy Data Specialist is a remote evaluation track for reviewing multilingual taxonomy data evaluation prompts and responses against AuraOne's quality rubric.

About the role

Multilingual Taxonomy Data Specialist is a remote evaluation track for reviewing multilingual taxonomy data 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 multilingual taxonomy data evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Design and audit label taxonomies, knowledge graphs, and evaluation datasets.

Responsibilities

• Evaluate multilingual taxonomy data evaluation model outputs against a versioned rubric and assign severity tags for Multilingual Taxonomy Data Specialist 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 multilingual taxonomy data evaluation or adjacent content for Multilingual Taxonomy Data Specialist 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 multilingual taxonomy data 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
• Multilingual Taxonomy Data evaluation
• Data quality
• Taxonomy design
• Ontology review
• Multilingual
• Taxonomy