[Hiring] AI Data Annotator, Entry Level REMOTE USA
Position: AI Data Annotator, Entry Level
Date Posted: September 6, 2026
Industry: Artificial Intelligence | Data Annotation | Technology
Employment Type: Full Time
Experience: Entry Level | Previous annotation, content review, research, or data-entry experience preferred
Qualification: Bachelor’s Degree, Associate Degree, Certification, or equivalent experience preferred
Salary: $40,000 – $60,000 per year (Estimated)
Location: REMOTE, United States
Company: Jobright.ai
Description:
Jobright.ai is looking for an Entry-Level AI Data Annotator to support the improvement of AI-powered job search products. The role focuses on reviewing, labeling, and evaluating text-based and structured data to improve the quality, accuracy, and usefulness of AI systems.
This is an excellent opportunity for detail-oriented professionals interested in artificial intelligence, data quality, and AI-driven products. The selected candidate will work remotely while contributing to the development of AI agents used by real users.
Key Responsibilities:
• Review, categorize, and annotate text-based and structured data.
• Apply detailed labeling guidelines accurately and consistently.
• Evaluate AI-generated content for relevance, quality, and policy alignment.
• Identify unclear examples, edge cases, and recurring data-quality issues.
• Document annotation decisions and provide feedback on guidelines.
• Participate in quality reviews and help resolve labeling disagreements.
• Meet accuracy and productivity targets in a remote work environment.
Requirements:
• Strong attention to detail and ability to follow written instructions.
• Clear written communication skills and sound judgment.
• Comfortable handling repetitive and detail-oriented tasks.
• Basic proficiency with spreadsheets and web-based tools.
• Ability to manage time independently in a remote environment.
Strong knowledge of:
• Data annotation and content review processes.
• AI, language models, job-search data, or taxonomy work.
• Quality metrics and structured review procedures.
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