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Personalized AI Response Evaluator

Review AI-generated responses that use email, calendar, photos, files, and business account context. Assess accuracy and personalization from Canada for 20 or more hours each week.

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OpenTrain AI

Generative AI & RLHF

Remote

1 country

Eligibility

Entry

Experience

Sep 22, 2026

Posted

Open to applicants in

Canada

The work

You will evaluate AI-generated responses using personalized prompts and information from connected applications. Your reviews will help improve how AI systems use context from email, calendars, photos, files, and business accounts.

You will follow evaluation guidelines and protect confidential information while writing clear, well-reasoned feedback.

  • Judge whether responses are relevant, accurate, helpful, and appropriately personalized.
  • Find unsupported assumptions, incorrect personalization, inconsistencies, and irrelevant recommendations.
  • Compare multiple responses and choose the one that provides the stronger user experience.
  • Give structured feedback about response quality and retrieval quality.

What it pays and takes

This is an entry-level contractor engagement that may continue for up to 16 weeks. Previous AI evaluation, data annotation, content review, quality assurance, or other analytical experience is helpful but not required.

  • Work 20 or more hours per week.
  • Be currently resident in Canada.
  • Use English for the evaluation work.
  • Have a bachelor's degree or equivalent practical experience in any field.
  • Use common productivity or consumer applications and have enough account history for context-based evaluation.
  • Be willing to connect eligible applications for personalized prompt evaluation.
  • Bring strong analytical judgment, careful attention to detail, and clear written communication.
  • Work independently with a reliable computer and internet connection.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

OpenTrain AI hires and contracts people for AI training and data-labeling work. In this field, people review and improve AI outputs so systems can give more accurate, useful responses; careful judgment and relevant experience matter because these reviews shape how AI behaves.

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