AI Data Quality Auditor for High-Volume Reviews

Talent Hackers, LLC

United States

Remote

USD 29,000 - 39,000

Full time

14 days+

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Job summary

Talent Hackers, LLC seeks a detail-oriented AI Data Quality Reviewer to ensure accuracy and integrity of AI training data. You will assess text and video submissions against guidelines, provide clear feedback, and help maintain dataset quality at scale.

Ideal candidates have 1–3+ years in data annotation or high-volume review, strong English communication, and the ability to work full-time during EST hours. You will contribute to consistent, scalable quality decisions across projects.

Qualifications

  • 1–3+ years of experience in data annotation or high-volume review.
  • Experience reviewing work against guidelines, rubrics, or SOPs.
  • Excellent attention to detail and ability to identify errors and quality issues.
  • Strong written and verbal English communication.
  • Able to work full-time EST hours.

Responsibilities

  • Review submissions across text and video against project-specific guidelines and acceptance criteria.
  • Approve, reject, or return submissions for correction with clear feedback.
  • Maintain high accuracy and meet throughput targets.
  • Identify suspicious, low-effort or manipulated submissions and escalate integrity issues.
  • Apply detailed review rubrics consistently across large volumes.
  • Record review decisions and rejection reasons precisely.

Skills

Attention to detail
English communication
Data annotation
Quality assurance
High-volume review

Job description

Talent Hackers, LLC seeks a detail-oriented AI Data Quality Reviewer to ensure accuracy and integrity of AI training data. You will assess text and video submissions against guidelines, provide clear feedback, and help maintain dataset quality at scale.

Ideal candidates have 1–3+ years in data annotation or high-volume review, strong English communication, and the ability to work full-time during EST hours. You will contribute to consistent, scalable quality decisions across projects.

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