The work
You will help assess how well AI handles music-related questions and answers. Your reviews should be accurate, clear, consistent, and supported by reliable evidence.
The role covers music theory, composition, genres, composers, artists, instruments, production, notation, music history, and current music trends.
- Create advanced prompts about music theory, genres, composers, artists, production, and instruments.
- Review AI-generated responses for factual accuracy, reasoning, completeness, and nuance.
- Find made-up information, logical problems, outdated details, and unusual edge cases.
- Build benchmark datasets and adversarial test cases for difficult evaluations.
- Give evidence-based feedback with reliable references.
- Work with AI researchers and maintain consistent annotation quality and documentation.
What it pays and takes
This is listed as an entry-level opportunity. The role requires strong music knowledge and careful written analysis, with prior experience and AI evaluation skills helpful.
- Compensation: Not disclosed.
- Schedule: 20+ hours per week.
- Engagement: Remote contractor assignment for an eight-week contract.
- Time overlap: At least four hours of Pacific Time overlap is required.
- Language: Excellent written English.
- Education: A master's degree or higher is required.
- Relevant backgrounds include music, music theory, musicology, ethnomusicology, music education, music production, audio engineering, performing arts, or related fields.
- Preferred experience: Three or more years in music education, performance, composition, production, journalism, research, content creation, audio engineering, or a related field.
- Skills: Strong research, analytical ability, attention to detail, and objective, evidence-backed review skills.
- Helpful experience includes large language models, generative AI, prompt engineering, or AI evaluation.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind systems that generate and understand content. People review model responses, find errors, and provide examples and feedback so AI systems improve; subject-matter experts are especially useful when accuracy and nuance depend on deep field knowledge.