Evaluate AI Music Composer candidates across 4 weighted areas: body of work, craft and technique, reliability and process, and presence with an audience. Body of work leads at 35%, so ask for released tracks, cues, or game loops where generative models featured: check credits, sync placements, PRO registrations, and whether they can. Use the rubric to compare role-specific evidence consistently.
For body of work, look for evidence the candidate shares a linked catalogue of shipped cues with clear model versus human authorship, plus placement details in games, ads, or film. For craft and technique, look for evidence the candidate moves fluidly between model output and hands-on arrangement, naming conditioning methods, fine-tuning runs, and the mastering chain used on delivery.
Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Body of work
35% weight
Ask for released tracks, cues, or game loops where generative models featured: check credits, sync placements, PRO registrations, and whether they can play stems and prompt histories.
Evidence to listen for
Has work that exists publicly and can be checked
States what they made versus what they contributed to
Can talk about range as well as their signature style
Knows how the work was received and by whom
Five-point scoring guide
1
Poor
Nothing finished or public; describes intentions rather than output.
2
Needs Improvement
Small or unverifiable body of work; unclear personal contribution.
3
Satisfactory
Real output with adequate range; reception described vaguely.
4
Very Good
Substantial verifiable work with clear ownership and range.
5
Excellent
Shares a linked catalogue of shipped cues with clear model versus human authorship, plus placement details in games, ads, or film.
02
Evaluation factor
Craft and technique
25% weight
Probe fluency across MusicGen, Suno, Udio, Stable Audio, or RAVE alongside real DAW work: MIDI editing, Kontakt libraries, mixing to broadcast LUFS targets.
Evidence to listen for
Can discuss technique concretely rather than in terms of feel or vibe
Names influences and what they took from each
Explains a deliberate choice and the effect it was for
Knows their own weak areas and what they do about them
Five-point scoring guide
1
Poor
No technical vocabulary; cannot discuss choices behind the work.
2
Needs Improvement
Talks in generalities; choices sound accidental.
3
Satisfactory
Solid craft with some ability to explain decisions.
4
Very Good
Articulate about technique and deliberate about effect.
5
Excellent
Moves fluidly between model output and hands-on arrangement, naming conditioning methods, fine-tuning runs, and the mastering chain used on delivery.
03
Evaluation factor
Reliability and process
25% weight
Test turnaround discipline on briefs: revision rounds, alternate lengths and stinger versions, stem and cue sheet delivery, and how they document training data provenance.
Evidence to listen for
Meets deadlines and briefs, with evidence
Handles revisions and direction without ego
Describes how they work when the brief is vague or changes late
Has repeat clients, bookings, or collaborators
Five-point scoring guide
1
Poor
Misses commitments; cannot take direction.
2
Needs Improvement
Inconsistent delivery; defensive about revisions.
3
Satisfactory
Reliable on clear briefs; struggles when direction shifts.
4
Very Good
Consistently delivers and adapts; repeat collaborators.
5
Excellent
Describes a repeatable pipeline hitting deadlines with versioned stems, clean cue sheets, and documented rights clearance on every generated element.
04
Evaluation factor
Presence with an audience
15% weight
Look for how they present drafts to directors, music supervisors, or game audio leads, and defend a choice when the client says the piece feels wrong.
Evidence to listen for
Comfortable performing or presenting on demand, including cold
Reads a room and adjusts
Handles a bad night, a difficult client, or a hostile crowd without unravelling
Represents the employer well in front of others
Five-point scoring guide
1
Poor
Cannot perform or present on request; unravels under pressure.
2
Needs Improvement
Rigid delivery; no read of the room.
3
Satisfactory
Competent in familiar settings; less adaptable in new ones.
4
Very Good
Strong presence and adapts to the room.
5
Excellent
Reads a vague brief back in musical terms, plays targeted alternates, and takes hard notes without losing the emotional intent of the cue.
Evidence-led prompts
Interview questions for a AI Music Composer
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Can you discuss projects where you used AI to generate music?
02
Have you deployed music generation models in a production environment?
03
Have you worked on real-time music generation or improvisation?
04
Have you integrated machine learning models with audio processing?
05
What is your approach to training models for music creation?
