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Welo Global

AI Data Quality Specialist – English (Philippines)

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Remote from
Philippines
Salary
USD 5–5 / hour
Department
Data Science & Analytics
Employment
Contract
Experience
Open level
Published
Apply before
30 Nov 2026
Listing views
33
Application actions
2
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AI Summary

The role, at a glance.

Welo Global seeks a freelance AI Data Quality & Annotation Specialist to evaluate English-language content and help improve large language models. The specialist will label sentiment, factual-consistency, and reasoning issues, score model outputs, and validate automated assessments against structured guidelines. Success requires advanced written English, rigorous analytical judgment, consistency, and close attention to detail across high task volumes. Prior annotation, AI-training, research, journalism, or linguistics experience is beneficial, while familiarity with LLM evaluation is preferred.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

3/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

3/5
IndependentCollaborative
AI insightThe work requires sustained concentration, nuanced English comprehension, and consistent application of detailed scoring standards. It is moderately challenging because judgments must remain accurate and well justified across many similar evaluation tasks.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per hour)Market rate
$4.50
US market range (per hour)$4–$8
AI insightThe hourly pay period is inferred from the job context; it was not explicitly stated by the employer. The ATS provides a USD 4.50 compensation amount but does not state a cadence. An hourly period is inferred from the freelance annotation context and the amount. Estimated market compensation for comparable Philippines-based freelance AI data annotation work is approximately USD 4.00–8.00 per hour.

Core skills

Skills and capabilities most closely associated with this opportunity.

Data annotationLLM evaluationQuality assuranceSentiment analysisFact checkingAnalytical reasoningEnglish writingAttention to detailContent labelingData validationCritical thinkingGuideline adherence
Sample interview questions
How would you evaluate whether an LLM response is factually consistent?

I would identify the response's key claims, compare them with the supplied source material or trusted reference context, and flag unsupported, contradictory, or misleading statements. I would then assign the appropriate score and document the evidence behind my decision.

How do you maintain consistency when reviewing a large volume of annotation tasks?

I use the written rubric as the primary reference, create brief decision notes for recurring edge cases, and periodically review prior decisions for alignment. If a case remains ambiguous, I follow the escalation process rather than applying an unsupported assumption.

Describe how you would handle a response with correct facts but flawed reasoning.

I would separate factual accuracy from reasoning quality according to the scoring framework. The response could receive a positive factual score but a lower reasoning score if its conclusion is unsupported, internally inconsistent, or reached through invalid logic.

What makes a written annotation justification useful?

A useful justification is concise, specific, and tied directly to the evaluation criteria. It identifies the relevant part of the output, explains why it meets or fails the guideline, and avoids subjective language that cannot be verified.

How would you respond if you found that an automated assessment disagreed with your review?

I would recheck the task against the rubric and source context, identify the precise point of disagreement, and record the discrepancy with supporting evidence. I would not change my judgment solely to match the automated result if the guidelines support my assessment.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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).

Compensation

Additional Information

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