We are sharing a specialised part-time consulting opportunity for experienced music and audio professionals with strong analytical listening, music theory, sound identification, and annotation skills to contribute to an advanced AI training project focused on real-world music and field-recording analysis.
Selected professionals will listen closely to short field recordings, identify musical and environmental details, compare recordings captured in different settings, and produce structured annotations through a dedicated web platform. Later project phases may involve evaluating AI-generated descriptions against the underlying audio and identifying statements that are correct, inaccurate, or unsupported.
Key Responsibilities
Analytical Listening & Audio Annotation
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Listen to short field recordings typically ranging from approximately 30–90 seconds
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Identify instruments present within complex or noisy real-world recordings
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Estimate musical tempo and identify key using professional listening skills
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Document audible background events such as traffic, sirens, applause, crowd reactions, or other environmental sounds
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Capture relevant scene details using structured annotation fields and concise written notes
Musical & Environmental Analysis
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Distinguish individual musical elements within dense or imperfect real-world recordings
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Apply music-theory knowledge when assessing tempo, key, instrumentation, and musical structure
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Identify when specific sounds or musical elements enter or change within a recording
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Use timestamps to document relevant events precisely
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Maintain consistent analytical standards across repeated listening assignments
Recording Comparison & Change Detection
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Compare paired recordings of the same song captured at different locations
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Determine which musical characteristics remain consistent between recordings
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Identify differences caused by environment, performance, acoustics, recording conditions, or background activity
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Document relevant changes clearly and objectively
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Apply evidence-based listening judgement rather than inference unsupported by the audio
AI Output Review & Quality Evaluation
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In later project phases, review AI-generated descriptions of audio recordings
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Identify statements that accurately reflect the recording
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Flag incorrect, unsupported, or invented details
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Provide brief written explanations supporting evaluation decisions
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Contribute precise human judgement that improves the quality of music and audio AI systems
Ideal Profile
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3+ years of hands-on professional, academic, or substantial practical experience in music or audio
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Relevant backgrounds may include working or gigging musicians, session players, producers, or mixing engineers
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Music educators, ear-training instructors, band or choir directors, conservatory students, or graduates are also highly relevant
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Experience as a location sound recordist, sound designer, foley artist, podcast editor, or documentary audio editor is strongly valued
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Strong analytical listening skills and ability to distinguish individual instruments within busy or noisy recordings
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Ability to estimate tempo within a few BPM
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Ability to identify musical key; relative pitch using a reference is acceptable
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Comfortable performing detailed and repetitive annotation work using web-based interfaces
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Strong attention to detail when working with dropdowns, timestamps, and structured notes
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Clear written English and ability to provide concise, evidence-based observations
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Access to quality headphones, a quiet working environment, and reliable internet
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No prior experience in AI training or model evaluation is required
Engagement Details
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Part-time independent contractor engagement
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Fully remote
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Compensation: $30–$45/hour
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Expected availability: approximately 20–25 hours per week
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Work will involve analytical listening, music and sound identification, timestamped annotation, recording comparison, and later-stage AI-output evaluation
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A paid onboarding and calibration session is included
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Participants should also be available for a short weekly synchronisation session
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Quality headphones and a quiet workspace are required for reliable evaluation
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Project phases, workload, annotation categories, and evaluation standards may evolve depending on project requirements
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Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party
About the Platform
This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.
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