Historical Reasoning AI Evaluator

AI Trainer Jobs; United States; Remote; USD 34,000 - 55,000; Part time

Job summary

AI Trainer Jobs seeks a Historical Reasoning AI Evaluator for AuraOne's quality rubric. This remote contractor role involves reviewing prompts and responses, comparing paired outputs, labeling edge cases, and providing structured feedback to retrain the model.

Responsibilities include evaluating outputs against the rubric, selecting the stronger answer with rationale, tagging issues, and maintaining calibration against gold-standard examples each week.

Qualifications

• Prior evaluation, annotation, or human-rater experience on historical reasoning AI evaluation or adjacent content.

Responsibilities

• Evaluate historical reasoning AI evaluation model outputs against a versioned rubric and assign severity tags for Historical Reasoning AI Evaluator 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.

Skills

Evaluation experience

Annotation experience

Written reasoning

Attention to detail

Async availability

Job description

Historical Reasoning AI Evaluator is a remote evaluation track for reviewing historical reasoning ai 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

Historical Reasoning AI Evaluator is a remote evaluation track for reviewing historical reasoning ai evaluation prompts and responses against AuraOne's quality rubric.

About the role

Historical Reasoning AI Evaluator is a remote evaluation track for reviewing historical reasoning ai 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 historical reasoning ai 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 historical reasoning ai evaluation model outputs against a versioned rubric and assign severity tags for Historical Reasoning AI Evaluator 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 historical reasoning ai evaluation or adjacent content for Historical Reasoning AI Evaluator 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 historical reasoning ai 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
• Historical Reasoning AI evaluation
• Learning design
• Assessment review
• Pedagogy
• Historical
• Reasoning