Music Content Analyst - AI Trainer
Handshake AI Fellow – Music Content Analyst
Location: Remote
Type: Contract / Fellowship
Team: Handshake AI
About the Role
Handshake AI is seeking detail-oriented Music Content Analysts (Fellows) to support the development and evaluation of AI systems that process and understand music. This role is ideal for individuals with formal education in music or professional experience as a musician who have strong listening skills and a deep understanding of musical structure and vocal performance.
Fellows will work directly with audio content to assess lyrical accuracy, vocal composition, and song structure.
Key Responsibilities
• Lyric Review & Correction
Listen carefully to song excerpts.
Identify errors or discrepancies in provided lyrics.
Correct lyrics for accuracy in wording, phrasing, and structure.
• Vocal Analysis
Determine the number of singers in a track.
Identify the gender of each vocalist based on audio analysis.
• Song Structure Identification
Classify excerpts into song sections (e.g., verse, chorus, bridge, intro, outro, pre-chorus) using a provided framework.
• Provide structured, high-quality annotations according to project guidelines.
• Maintain consistency and accuracy across evaluations.
Qualifications (Required)
Bachelor’s degree in Music, Music Theory, Music Performance, Music Education, or a closely related field OR
Professional experience as a performing, recording, or touring musician.
Strong ear for pitch, harmony, vocal layering, and arrangement.
Familiarity with common song structures across genres.
High attention to detail and ability to work independently.
Preferred Qualifications
Experience with transcription or lyric annotation.
Background in vocal performance or choral work.
Experience in studio recording or music production.
Prior experience with annotation, QA, or AI training projects.
Ideal Candidate
You are an active listener with a trained ear. You can quickly distinguish vocal layers, identify structural transitions in songs, and spot lyrical inaccuracies with precision. You are methodical, reliable, and comfortable working within structured evaluation frameworks.
Location: Remote
Type: Contract / Fellowship
Team: Handshake AI
About the Role
Handshake AI is seeking detail-oriented Music Content Analysts (Fellows) to support the development and evaluation of AI systems that process and understand music. This role is ideal for individuals with formal education in music or professional experience as a musician who have strong listening skills and a deep understanding of musical structure and vocal performance.
Fellows will work directly with audio content to assess lyrical accuracy, vocal composition, and song structure.
Key Responsibilities
• Lyric Review & Correction
Listen carefully to song excerpts.
Identify errors or discrepancies in provided lyrics.
Correct lyrics for accuracy in wording, phrasing, and structure.
• Vocal Analysis
Determine the number of singers in a track.
Identify the gender of each vocalist based on audio analysis.
• Song Structure Identification
Classify excerpts into song sections (e.g., verse, chorus, bridge, intro, outro, pre-chorus) using a provided framework.
• Provide structured, high-quality annotations according to project guidelines.
• Maintain consistency and accuracy across evaluations.
Qualifications (Required)
Bachelor’s degree in Music, Music Theory, Music Performance, Music Education, or a closely related field OR
Professional experience as a performing, recording, or touring musician.
Strong ear for pitch, harmony, vocal layering, and arrangement.
Familiarity with common song structures across genres.
High attention to detail and ability to work independently.
Preferred Qualifications
Experience with transcription or lyric annotation.
Background in vocal performance or choral work.
Experience in studio recording or music production.
Prior experience with annotation, QA, or AI training projects.
Ideal Candidate
You are an active listener with a trained ear. You can quickly distinguish vocal layers, identify structural transitions in songs, and spot lyrical inaccuracies with precision. You are methodical, reliable, and comfortable working within structured evaluation frameworks.