Conversational AI Evaluator

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

Job summary

AI Trainer Jobs is seeking a remote Conversational AI Evaluator to review prompts and model outputs against a evolving rubric. You will compare paired responses, label errors, and provide structured feedback to support retraining efforts.

The role emphasizes clear reasoning, detailed marking, and consistent rubric application with at least 10 hours per week of availability as an independent contractor. Hourly pay is determined after interview.

Qualifications

• Prior evaluation, annotation, or human-rater experience on conversational ai evaluation.
• Clear written reasoning that names the issue and the rubric clause.
• Strong attention to detail and the ability to flag when a prompt itself is the problem.
• Ability to apply multi-page rubrics consistently across long batches.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate conversational ai 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 back to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples each week.

Skills

Conversational AI evaluation

Inter-rater calibration

Attention to detail

Structured feedback writing

Education

Linguistics background

Content moderation / trust & safety

Job description

About the role

Conversational AI Evaluator is a remote evaluation track for reviewing conversational 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 conversational 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 conversational ai evaluation model outputs against a versioned rubric and assign severity tags for Conversational 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 conversational ai evaluation or adjacent content for Conversational 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 conversational 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
• Conversational AI evaluation
• Voice, language and multimodal
• AI evaluation
• Rubric writing
• Expert review