Evaluate AI Video Editor 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 reels and links: short form ads, YouTube long form, AI generated b-roll. Check view counts, retention graphs, and which cuts. Use the rubric to compare role-specific evidence consistently.
For body of work, look for evidence the candidate shows shipped work with named clients or channels, states their exact role per piece, and cites retention or watch-time numbers. For craft and technique, look for evidence the candidate explains prompt-to-timeline workflow, fixes AI artefacts like morphing hands or flicker, and handles LUTs, keyframes, and loudness normalisation.
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 reels and links: short form ads, YouTube long form, AI generated b-roll. Check view counts, retention graphs, and which cuts they personally edited versus supervised.
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
Shows shipped work with named clients or channels, states their exact role per piece, and cites retention or watch-time numbers.
02
Evaluation factor
Craft and technique
25% weight
Test hands-on command of Premiere or Resolve plus AI stack: Runway, Kling, Veo, Topaz upscaling, ElevenLabs voice, Descript transcript edits, plus colour and audio finishing.
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
Explains prompt-to-timeline workflow, fixes AI artefacts like morphing hands or flicker, and handles LUTs, keyframes, and loudness normalisation.
03
Evaluation factor
Reliability and process
25% weight
Probe throughput under deadline: cuts delivered per week, revision rounds, project and asset naming, proxy workflows, Frame.io review cycles, and render or delivery spec discipline.
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
Quotes realistic turnaround volumes, keeps versioned organised projects, and delivers correct codecs and aspect ratios without chasing.
04
Evaluation factor
Presence with an audience
15% weight
Judge instinct for the viewer: how they build hooks in the first three seconds, pace jump cuts, use captions and sound design, and defend edits to clients.
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
Articulates why a cut holds attention, references A/B tested hooks or thumbnails, and takes client notes without losing narrative logic.
Evidence-led prompts
Interview questions for a AI Video Editor
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Can you describe a challenging project where you used automated editing techniques?
02
Can you provide examples of projects where you successfully used these tools for editing?
03
What is the largest project you have worked on using these tools?
04
Can you describe how you used automation for recurring tasks in editing?
05
Can you describe an instance where automation significantly improved your editing efficiency?
