Remote Algorithmic Complexity Evaluation Expert

AI Trainer Jobs; United States; Remote; USD 34,000 - 62,000; Part time

Job summary

AI Trainer Jobs is seeking a remote Algorithmic Complexity Reasoning Reviewer to assess prompts and responses against AuraOne's rubric. You will compare outputs, label edge cases, and provide structured feedback for retraining the model.

This is a contractor role with hourly compensation to be confirmed after interview. Responsibilities include evaluating model outputs, tagging issues, and flagging ambiguous prompts.

Qualifications

• Prior evaluation, annotation, or human-rater experience on algorithmic complexity reasoning 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 algorithmic complexity reasoning evaluation model outputs against a versioned rubric and assign severity tags for Algorithmic Complexity Reasoning Reviewer 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.

Skills

Evaluation experience

Annotation

Inter-rater calibration

Attention to detail

Written reasoning

Async availability

Tools

Rubric-based annotation

Job description

AI Trainer Jobs is seeking a remote Algorithmic Complexity Reasoning Reviewer to assess prompts and responses against AuraOne's rubric. You will compare outputs, label edge cases, and provide structured feedback for retraining the model.

This is a contractor role with hourly compensation to be confirmed after interview. Responsibilities include evaluating model outputs, tagging issues, and flagging ambiguous prompts.