Unit Test Generation Workflow Reviewer

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

Job summary

AuraOne is seeking a remote Unit Test Generation Workflow Reviewer to evaluate model outputs against a fixed rubric and provide structured feedback for retraining. This independent contractor role emphasizes identifying edge cases, labeling errors, and writing concise rationales to guide the modeling team.

Reviewers compare paired responses, tag halos, and help calibrate evaluators across multi-page rubrics, contributing to robust audit trails and quality control in unit test generation

Qualifications

• Experience evaluating AI model outputs against rubrics.
• Ability to assign severity tags to outputs.
• Ability to provide structured feedback for retraining.

Responsibilities

• Evaluate unit test generation workflow outputs against a versioned rubric.
• Compare paired responses and select the stronger with rationale.
• Label hallucinations, instruction-following failures, and unsafe content with tags.
• Capture ambiguous prompts and route for rubric updates.

Skills

Model evaluation

Rubric annotation

Edge-case labeling

Written rationale

Inter-rater calibration

Tools

Rubric-based tools

Job description

Unit Test Generation Workflow Reviewer is a remote evaluation track for reviewing unit test generation workflow 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

Unit Test Generation Workflow Reviewer is a remote evaluation track for reviewing unit test generation workflow evaluation prompts and responses against AuraOne's quality rubric.

About the role

Unit Test Generation Workflow Reviewer is a remote evaluation track for reviewing unit test generation workflow 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 unit test generation workflow 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 unit test generation workflow evaluation model outputs against a versioned rubric and assign severity tags for Unit Test Generation Workflow 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.

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 unit test generation workflow evaluation or adjacent content for Unit Test Generation Workflow Reviewer 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 unit test generation workflow 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
• Unit Test Generation Workflow evaluation
• Code review
• Software testing
• Agent evaluation
• Unit
• Test
• Generation