Music Professional AI Training Expert

You will help improve AI systems that generate and discuss music. The work uses your practical knowledge of performance, composition, production, and music concepts to judge whether model outputs are accurate and relevant.

You will work independently in a remote, asynchronous setting and provide careful written feedback on generated content.

• Create expert-level training data using professional music knowledge.
• Evaluate AI-generated responses for accuracy, relevance, and alignment with music concepts and performance practice.
• Review outputs using knowledge of composition, instrumental performance, jazz, guitar, commercial music, or a related discipline.
• Write clear feedback and make careful judgments about generated content.

This is an ongoing, project-based contract opportunity that can be taken on alongside other commitments. The role is focused on music performance and practical music expertise rather than primarily musicology or ethnomusicology study.

• Pay: up to $100 per hour.
• Location: Open to candidates in the United States.
• Schedule: 20+ hours per week; the work is remote, flexible, and asynchronous.
• Education: A master's degree or higher in a relevant music field is required; a DMA is strongly preferred.
• Experience: Strong background in instrumental performance, composition, jazz performance, guitar performance, commercial music, or a related performance discipline.
• Skills: Strong written communication, attention to detail, and the ability to work independently.
• Helpful tools: Experience with Pro Tools, Logic Pro, or Ableton is useful but not required.
• Helpful background: Experience across different musical styles, traditions, or cultures can support the quality of expert feedback.

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to join and helps people find AI training and data-labeling work in one place.

AI training is the human work behind artificial intelligence. People create examples, review model responses, and give feedback so AI systems can produce more accurate and useful results. Experienced contributors are valued when a project needs sound judgment in a specialist field such as music.