AI Work Central

AI Data Quality Specialist - English (Philippines)

WelocalizeLanguagesFirst seen 9 Oct 2026

$4.50/hr as listed by Welocalize, USDEstimate a week at this rate
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Where
Philippines
Language
English
Type
Contract, remote

Posted on Welocalize First seen 9 Oct 2026Checked 12 Oct 2026

From the Welocalize listing

Contract Type: Freelance

Language: English (Philippines)

About the Role

We are seeking a detail-oriented AI Data Quality & Annotation Specialist to help develop and evaluate large language models (LLMs). Your role will involve reviewing English language content to assess sentiment accuracy, factual consistency, and reasoning quality using structured guidelines. This position is ideal for individuals with sharp analytical skills, attention to detail, and the ability to consistently apply labeling standards.

What You Will Do:

- Review and label content for sentiment, factual accuracy, and reasoning issues.

- Evaluate model outputs across quality dimensions using scoring frameworks.

- Validate automated assessments and identify discrepancies or errors.

Requirements:

  • Advanced English proficiency (reading and writing) with professional-level fluency.
  • Strong comprehension and articulation skills, with the ability to provide clear and well-reasoned written justifications.
  • Previous experience in data annotation, AI training, or related projects.
  • Preferred experience in analytical fields such as linguistics, journalism, research, or similar domains.
  • Exceptional attention to detail and accuracy.
  • Proven analytical reasoning and critical thinking abilities.
  • Consistency in decision-making across high volumes of tasks.
  • Familiarity with AI systems, large language models (LLMs), or evaluation processes (preferred but not required).

Text above is the platform's own listing, shown as published. Check the details on Welocalize before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Annotation: Adding labels or notes to data such as text, images, audio or video so a model can learn from it.
  • Guidelines: The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
  • Labeling: Assigning a category or tag to an item, for example marking a review as positive or negative. Often used as a synonym for annotation.
  • Assessment: Any screening step a platform uses before giving you work: a quiz, a skills test, a language test, a writing sample or an AI interview.

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