Web Browsing Evaluator

Web Browsing Evaluator is a remote evaluation track for reviewing web browsing evaluation prompts and responses against Aura One'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 Browsing Evaluator is a remote evaluation track for reviewing web browsing evaluation prompts and responses against Aura One's quality rubric.

Web Browsing Evaluator is a remote evaluation track for reviewing web browsing evaluation prompts and responses against Aura One'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 browsing evaluation outputs into auditable labels, rationales, and regression cases for Aura One Human Data.
Review browser automation and multi-step search agents. Check whether sources hold up.

• Evaluate web browsing evaluation model outputs against a versioned rubric and assign severity tags for Web Browsing 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.

• Prior evaluation, annotation, or human-rater experience on web browsing evaluation or adjacent content for Web Browsing 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.

• Compare two web browsing 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.

• 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.

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.

• Model output evaluation
• Rubric-based annotation
• Severity tagging
• Inter-rater calibration
• Web Browsing evaluation
• AI model evaluation