Web Browsing Evaluator

AuraOne Human Data; United States; Remote; USD 21,000 - 34,000; Full time

Job summary

AuraOne Human Data is seeking a Web Browsing Evaluator for a remote role. You will review prompts and responses against rubrics, label edge cases, and provide structured feedback to retrain models.

Responsibilities include evaluating model outputs, comparing paired responses, labeling hallucinations and unsafe content, and surfacing prompts for rubric updates. Requires reliable async availability for at least 10 hours per week.

Qualifications

• Experience in evaluation, annotation, or human-rater tasks for web browsing.
• Ability to consistently apply rubrics across long batches.
• Clear written reasoning that cites rubric clauses.
• Strong attention to detail and the ability to flag problematic prompts.
• Reliable async availability for at least 10 hours per week.

Responsibilities

• Evaluate web browsing model outputs against a versioned rubric and assign severity tags.
• Compare paired responses and select the stronger answer with a rationale.
• Label hallucinations, instruction-following failures, and unsafe content with structured tags.
• Capture ambiguous prompts and route to the program team for rubric updates.
• Maintain reviewer-quality scores by calibrating against gold-standard examples.
• Document recurring failures to guide next training runs.

Skills

Evaluation experience

Annotation

Attention to detail

Written reasoning

Job description

US Remote

About the role

Web Browsing Evaluator is a remote evaluation track for reviewing web browsing 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 browsing 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 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.
• Document recurring failure modes so the modeling team can target them in the next training run.

What you should bring

• 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.