AI Written Response Evaluator and Assessor

You will assess AI-generated written responses and decide how well they meet defined standards. Your reviews will help improve AI systems by showing where answers are clear, useful, accurate, well-supported, and appropriate for their audience.

You will work independently and collaborate asynchronously through digital content review platforms.

• Compare multiple AI-generated responses for clarity, tone, helpfulness, and instruction-following.
• Use rubrics and guidelines to assign analytical quality scores.
• Write detailed rationales that explain each decision and identify supporting evidence.
• Find factual errors, internal inconsistencies, unsupported claims, and other reliability issues.
• Recognize answers that sound fluent but lack support, or that are comprehensive but impractical for their audience.
• Keep thorough records of your observations and decisions.

This is a remote, part-time contractor engagement. The listing identifies the role as entry level, but the work requires experience reviewing written work against defined standards.

• Pay: $90-$140 USD per hour.
• Time: 20 or more hours per week.
• Language:
  First-language English proficiency or fully equivalent written fluency.
• Education:
  
  A completed degree from an accredited institution in the United States, Canada, United Kingdom, Ireland, Australia, or New Zealand.
• Experience:
  
  Marking, grading, examining, or reviewing written work using criteria or rubrics.
• Relevant background:
  Assessment design, instructional design, peer review, thesis supervision, or academic writing support.
• Skills:
  
  Strong attention to detail, critical analysis, objective judgment, and clear written explanations.
• Work style:
  Ability to work independently and meet project deadlines remotely.
• Helpful but not required:
  Experience with model evaluation, reinforcement learning from human feedback, or data annotation.

AI training is the human work behind systems that generate and understand text. Reviewers compare model responses, apply clear standards, and explain their decisions so AI systems can become more accurate, useful, and reliable.