Web Scraper

AI Trainer Jobs; United States; Remote; USD 41,000 - 83,000; Part time

Job summary

AuraOne seeks a remote Web Scraper evaluator to review prompts and responses against its quality rubric. You will compare paired outputs, label edge cases, and craft structured feedback for model retraining.

As a contractor, you will help turn evaluation outputs into auditable labels, rationales, and regression cases, while auditing batches and calibrating scores weekly. Strong attention to detail is essential.

Qualifications

• Prior evaluation, annotation, or human-rater experience on web scraper evaluation.
• Clear written reasoning that names the issue and rubric clause.
• Reliable async availability for at least 10 hours per week.
• Experience with inter-rater agreement metrics.

Responsibilities

• Evaluate web scraper evaluation model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and pick the stronger answer with 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

Model output evaluation

Rubric-based annotation

Severity tagging

Inter-rater calibration

Web Scraper evaluation

Job description

Web Scraper is a remote evaluation track for reviewing web scraper 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: Search, Web & Browser Agents · Pay: $30–$60 / hr · Location: Remote — US-eligible · Contractor

Web Scraper is a remote evaluation track for reviewing web scraper evaluation prompts and responses against AuraOne's quality rubric.

Role details

About the role

Web Scraper is a remote evaluation track for reviewing web scraper 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 web scraper evaluation outputs into auditable labels, rationales, and regression cases for AuraOne Human Data.
Review browser automation and multi-step search agents. Check whether sources hold up.

Responsibilities

• Evaluate web scraper evaluation model outputs against a versioned rubric and assign severity tags for Web Scraper 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 web scraper evaluation or adjacent content for Web Scraper 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 web scraper 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
• Web Scraper evaluation