Remote Sampling Bias Ontology QA Specialist

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

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.