Sampling Bias Ontology QA Reviewer

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

Benefits offered by this job

Remote work

Contractor

US-eligible

Job summary

AuraOne seeks a remote Sampling Bias Ontology QA Reviewer to evaluate prompts and responses against its quality rubric. You will compare paired outputs, label edge cases, and write structured feedback to support retraining of models.

AI data reviewers help convert evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data, while designing and auditing label taxonomies, knowledge graphs, and evaluation datasets.

Qualifications

• Prior evaluation, annotation, or human-rater experience on sampling bias ontology QA reviewer work.
• Ability to apply multi-page rubrics consistently across long batches.
• Clear written reasoning that names the issue and rubric clause being applied.
• Strong attention to detail and the ability to flag prompts that are the problem.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate sampling bias ontology QA evaluation model outputs against a versioned rubric and assign severity tags.
• 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 to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples.

Skills

Evaluation experience

Annotation

Inter-rater calibration

Rubric-based annotation

Data quality

Linguistics background

Trust & safety review

Bias detection

Job description

Sampling Bias Ontology QA Reviewer is a remote evaluation track for reviewing sampling bias ontology qa 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

Sampling Bias Ontology QA Reviewer is a remote evaluation track for reviewing sampling bias ontology qa evaluation prompts and responses against AuraOne's quality rubric.

About the role

Sampling Bias Ontology QA Reviewer is a remote evaluation track for reviewing sampling bias ontology qa 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 sampling bias ontology qa 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 sampling bias ontology qa evaluation model outputs against a versioned rubric and assign severity tags for Sampling Bias Ontology QA 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.

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 sampling bias ontology qa evaluation or adjacent content for Sampling Bias Ontology QA Reviewer 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 sampling bias ontology qa 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
• Sampling Bias Ontology QA evaluation
• Data quality
• Taxonomy design
• Ontology review
• Sampling
• Bias