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Top pay

AI Data Quality Specialist - English (Philippines)

Welo Global · company site
Posted 6 months ago — may be filled

1 · Can you apply from United States?

Not open

The ad doesn’t say — see the full posting.

2 · What reaches you

1Pay
2Platform fee− $0
3Payout fee · Wise− $0 – $6.11
≈ In your money
$774 / month
Compare with what I earn now

Wise fee page, checked 25 Sep 2026
Fixed fees assume one withdrawal a month.

3 · How you get paid

Unknown — ask the company

How often
Unknown
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First money
Unknown
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4 · Your working hours

Not stated

The ad doesn’t say which hours. Ask the company.

5 · Trust

Company site · found 28 Sep 2026
No one should ask you to pay to work.
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They ask for

Experience not statedNo degreeEnglish C1+Data annotation

Full description

Shown as posted, in English

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).

Apply on the company site
Top pay
GigWelo Global · company site

AI Data Quality Specialist - English (Philippines)

Posted 6 months ago — may be filled

1 · Can you apply from United States?

Not open

The ad doesn’t say — see the full posting.

4 · Your working hours

Not stated

The ad doesn’t say which hours. Ask the company.

5 · Trust

Company siteFound 28 Sep 2026
No one should ask you to pay to work.
Something wrong?Report this post

They ask for

Experience not statedNo degreeEnglish C1+Data annotation

Full description

Shown as posted, in English

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).