Remote Economics Professor AI Trainer Evaluator

AuraOne Human Data; United States; Remote; USD 34,000 - 69,000; Part time

Benefits offered by this job

Remote work

Contractor role

US eligibility

Job summary

AuraOne, Inc. is seeking a remote Economics Professor - AI Trainer (Contract) to review evaluation prompts and responses against our quality rubric.

You will compare paired outputs, label edge cases, and provide structured feedback for retraining. Responsibilities include evaluating model outputs against a versioned rubric, tagging severities, and documenting recurring failure modes to help the modeling team improve future training runs.

Qualifications

• Prior evaluation, annotation, or human-rater experience on economics ai trainer evaluation or adjacent content.
• 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.

Responsibilities

• Evaluate economics professor ai trainer evaluation model outputs against a versioned rubric and assign severity tags for Economics Professor - AI Trainer (Contract) 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.
• Document recurring failure modes so the modeling team can target them in the next training run.

Skills

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Economics professor AI trainer eval

AI model evaluation

LLM experience

Analytical thinking

Job description

AuraOne, Inc. is seeking a remote Economics Professor - AI Trainer (Contract) to review evaluation prompts and responses against our quality rubric.

You will compare paired outputs, label edge cases, and provide structured feedback for retraining. Responsibilities include evaluating model outputs against a versioned rubric, tagging severities, and documenting recurring failure modes to help the modeling team improve future training runs.