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About Turing:

Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.

Role Overview:

We are seeking a highly qualified Music Domain Reviewer to support the quality assurance of Large Language Model (LLM) evaluation projects. In this role, you will review domain-specific prompts, evaluate completed tasks for accuracy and quality, ensure adherence to project guidelines, and provide actionable feedback to maintain high annotation standards. Your expertise in music will help ensure the reliability, consistency, and quality of AI evaluation data across diverse music-related topics.

Key Responsibilities:

• Review and validate domain-specific prompts covering music theory, genres, composers, artists, music history, production, instruments, and related topics.
• Evaluate completed tasks to ensure factual accuracy, reasoning quality, completeness, and compliance with project guidelines.
• Identify factual inaccuracies, logical inconsistencies, hallucinations, outdated information, and low-quality annotations.
• Ensure prompts are challenging, relevant, and aligned with project objectives.
• Provide clear, constructive, and evidence-based feedback to contributors to improve task quality.
• Maintain consistency across reviews by following established quality standards and review guidelines.
• Escalate ambiguous or complex cases when necessary and document review findings.
• Collaborate with project managers and AI teams to continuously improve evaluation quality and review processes.

Minimum Qualifications:

• Master's degree or higher in any field; degrees in Music, Music Theory, Musicology, Ethnomusicology, Music Education, Music Production, Audio Engineering, Performing Arts, or other music-related disciplines are preferred.
• Strong knowledge of music, including music theory, composition, genres, composers, artists, instruments, music history, production techniques, notation, and contemporary music trends.
• 3+ years of relevant professional experience, preferably in music education, performance, composition, music production, journalism, research, content creation, audio engineering, or a related field.
• Excellent written English, research, and analytical skills.
• Strong attention to detail and ability to evaluate music-related information accurately, identify factual inconsistencies, and assess reasoning across diverse musical topics.

Preferred Qualifications:

• Experience reviewing AI-generated content, LLM evaluations, prompt engineering, or annotation quality.
• Familiarity with quality assurance, editorial review, or content evaluation workflows.
• Strong knowledge of music theory, genres, composers, artists, instruments, music production, and music history.
• Ability to provide objective, evidence-based feedback while maintaining consistent quality standards.
• Experience working independently in a fast-paced, quality-focused environment.

Offer Details:

• Commitments Required: 40 hours per week with at least 4 hours PST overlap
• Employment type : Contractor assignment (no medical/paid leave)
• Duration of contract : 8 weeks.

Application Process:

• Complete the assessment shared with you.
• Our team will evaluate your submission.
• Selected candidates will be contacted regarding the next steps.