AI Trainer

AI Trainer Jobs; United States; Remote; USD 28,000 - 55,000; Part time

Job summary

AI Trainer is a remote evaluation track for reviewing AI trainer evaluation prompts and responses against AuraOne’s quality rubric. Reviewers compare paired outputs, label edge cases, and write structured feedback for retraining the model.

Responsibilities include evaluating model outputs, tagging issues, and calibrating quality weekly. This is a remote contractor role with flexible hours; hourly rate is discussed after the interview process.

Qualifications

• Prior evaluation, annotation, or human-rater experience for AI Trainer work.
• Clear written reasoning that names the issue and the rubric clause being applied.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate AI trainer model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route them back for rubric updates.
• Calibrate reviewer quality against gold-standard examples weekly.

Skills

Evaluation experience

Annotation

Rubrics

Attention to detail

Async availability

Job description

AI Trainer is a remote evaluation track for reviewing ai trainer 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

Role details

About the role

AI Trainer is a remote evaluation track for reviewing ai trainer evaluation prompts and responses against AuraOne's quality rubric.

AI Trainer is a remote evaluation track for reviewing ai trainer 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 ai trainer 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 ai trainer evaluation model outputs against a versioned rubric and assign severity tags for AI Trainer 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.

What you should bring

• Prior evaluation, annotation, or human-rater experience on ai trainer evaluation or adjacent content for AI Trainer 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 ai trainer 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
• AI Trainer evaluation
• AI model evaluation