AI Data Quality Reviewer

Talent Hackers, LLC; United States; Remote; USD 29,000 - 39,000; Full time

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

Job Overview

We are seeking a detail-oriented AI Data Quality Reviewer to help ensure the quality, accuracy, and integrity of data used in AI training and evaluation projects. In this role, you will review contributor submissions across text and video formats, assess them against project-specific guidelines, and make consistent quality decisions at scale.

The ideal candidate has experience working in high-volume review environments, possesses exceptional attention to detail, and can apply detailed guidelines consistently while maintaining productivity targets. You will play a critical role in maintaining dataset quality, identifying suspicious or low-quality submissions, and providing clear feedback to contributors.

Core Tasks:

• Review contributor submissions across text and video against project-specific guidelines and acceptance criteria
• Approve, reject, or return submissions for correction while providing clear and feedback
• Maintain high accuracy and consistency while meeting project throughput and turnaround-time targets
• Identify duplicate, low-effort, synthetic, manipulated, or otherwise suspicious submissions and escal potential integrity issues
• Apply detailed review rubrics consistently across large volumes of submissions
• Track recurring contributor errors and flag unclear or ineffective task instructions
• Accurately record review decisions, rejection reasons, and other required information
  

Must Have:

• 1-3+ years of experience in data annotation, quality assurance, content moderation, trust & safety, document review, or another high-volume review environment
• Experience reviewing the work or submissions of other people against defined guidelines, policies, rubrics, or SOPs
• Exceptional attention to detail and ability to identify subtle errors, inconsistencies, and quality issues
• Strong judgment and ability to make consistent decisions when reviewing large volumes of submissions
• Ability to quickly learn and accurately apply new project-specific guidelines and acceptance criteria
• Strong (C-level) written and verbal English communication skills
• Ability to provide concise, clear, and constructive reviewer feedback
• Comfortable working toward measurable accuracy, productivity, and turnaround targets
• Strong organizational skills and the ability to accurately document review decisions
• Ability to work full-time EST hours
  

Nice to Have:

• Previous experience reviewing AI training data, data annotations, RLHF tasks, or human-generated datasets
• Background in content moderation, trust & safety, fraud detection, data integrity, or marketplace quality control