Compensation Analyst - AI Trainer (Contract)

HandshakeSan Antonio, TX
1dRemote

About The Position

Handshake is seeking experienced Compensation Analysts to support AI research through flexible, hourly contract work. This is not a traditional full-time HR or Total Rewards role. You’ll use your real-world compensation and rewards experience to evaluate AI-generated content and provide feedback that helps AI better understand compensation analysis, methodologies, and professional HR language. This is an ongoing, project-based opportunity that can be done alongside your primary employment. Who This Is For This project is designed for professionals who are already working (or have recently worked) in roles such as: Compensation Analyst or Senior Compensation Analyst Total Rewards or Rewards Analyst with a strong focus on compensation HR Analyst specializing in pay structures, benchmarking, or job evaluation Compensation Consultant in an in-house or advisory capacity This is not a traditional full-time role. You’ll apply once and, if qualified, be considered for part-time, project-based work as new projects become available. What You’ll Do This project involves using your professional experience as a Compensation Analyst to design job-related questions and review AI-generated responses for accuracy and relevance to real-world compensation analysis and rewards work. No prior AI or technical experience is required.

Requirements

  • 4+ years of professional experience in compensation analysis or closely related compensation-focused HR roles
  • Hands-on experience conducting market pricing, supporting pay structures, and preparing compensation analyses or recommendations
  • Strong written communication skills and attention to detail
  • Comfortable working independently and following written guidelines
  • Professional judgment, reliability, and a high standard of discretion and confidentiality, especially with sensitive or proprietary information.

Responsibilities

  • design job-related questions
  • review AI-generated responses for accuracy and relevance to real-world compensation analysis and rewards work
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