[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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