Remote Deck QA Specialist β€” AI Content Review

Remote Deck QA Specialist β€” AI Content Review

Freelance 25 - 35 Β£ / hour (est.) Working from home possible
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At a Glance

  • Tasks: Review AI-generated documents for quality and accuracy in a remote setting.
  • Company: Join Obsidian, a leader in innovative AI content evaluation.
  • Benefits: Flexible hours, competitive pay, and the chance to work from anywhere.
  • Other info: Ideal for detail-oriented individuals with a Master's degree or higher.
  • Why this job: Make a difference by ensuring high-quality AI outputs while working remotely.
  • Qualifications: 5+ years of experience and proficiency in Microsoft Office and Google Workspace.

The predicted salary is between 25 - 35 Β£ per hour.

Obsidian is seeking expert Evaluators in Document/deck production QA to review AI-generated outputs for quality. This remote, hourly position requires over 5 years of experience and proficiency in Microsoft Office and Google Workspace. You will assess documents, spreadsheets, and presentations for accuracy. The ideal candidate should also possess a Master's degree or higher. If you're detail-oriented and can provide structured feedback, we encourage you to apply.

Remote Deck QA Specialist β€” AI Content Review employer: Obsidian

At Obsidian, we pride ourselves on fostering a collaborative and innovative work culture that empowers our employees to excel in their roles. As a Remote Deck QA Specialist, you will enjoy the flexibility of working from anywhere while contributing to high-quality AI content review, with ample opportunities for professional growth and development. Our commitment to employee well-being and continuous learning makes us an exceptional employer for those seeking meaningful and rewarding work.

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Contact Details:

Obsidian Recruitment Team

We think you need these skills to ace Remote Deck QA Specialist β€” AI Content Review

Document Production QA
AI Content Review
Microsoft Office Proficiency
Google Workspace Proficiency
Attention to Detail
Structured Feedback
Accuracy Assessment