Interview scorecard template

Deepfake Detection Engineer interview scorecard

Evaluate Deepfake Detection Engineer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so probe hands-on work with detection backbones and datasets: FaceForensics++, DFDC, Celeb-DF, ASVspoof for audio, plus frequency-domain artefacts, GAN fingerprints, PRNU and face-warping cues. Use the rubric to compare role-specific evidence consistently.

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software datacomputer visiondeepfake detectionmedia forensicsmodel evaluation
TL;DR
For technical proficiency, look for evidence the candidate names specific architectures and datasets used, explains which synthesis artefacts their models keyed on, and discusses failure modes candidly. For systems and trade-offs, look for evidence the candidate reasons about precision at volume, throughput limits and provenance metadata together, choosing a tiered pipeline rather than one monolithic classifier. 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

Technical proficiency

35% weight

Probe hands-on work with detection backbones and datasets: FaceForensics++, DFDC, Celeb-DF, ASVspoof for audio, plus frequency-domain artefacts, GAN fingerprints, PRNU and face-warping cues.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Names specific architectures and datasets used, explains which synthesis artefacts their models keyed on, and discusses failure modes candidly.

02
Evaluation factor

Systems and trade-offs

25% weight

Test how they balanced recall against false positives at platform scale, plus inference cost: TensorRT or ONNX latency budgets, frame sampling, cascade tiers, and C2PA provenance signals as complements.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Reasons about precision at volume, throughput limits and provenance metadata together, choosing a tiered pipeline rather than one monolithic classifier.

03
Evaluation factor

Evidence and rigour

25% weight

Assess evaluation discipline: cross-generator and cross-dataset AUC or EER, held-out unseen synthesis methods, compression and re-encoding robustness, adversarial and laundering attacks against their own detector.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Reports cross-dataset generalisation gaps with numbers, red-teams their own model, and distrusts in-distribution accuracy as a shipping signal.

04
Evaluation factor

Collaboration and communication

15% weight

Check how they hand findings to trust and safety reviewers, legal or policy teams: confidence calibration, explanation artefacts, heatmaps, and escalation thresholds for contested media.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Translates model scores into calibrated, reviewable evidence non-technical moderators act on, and states uncertainty instead of asserting fake or real.

Evidence-led prompts

Interview questions for a Deepfake Detection Engineer

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Do you have experience developing detection algorithms for synthetic media?

  2. 02

    Can you provide examples of research or projects you have carried out in this field?

  3. 03

    Do you have experience with both image and video detection?

  4. 04

    Can you explain some model architectures used for detecting synthetic media?

  5. 05

    Describe any experience with generation techniques and how it improved your detection work.

See the complete Deepfake Detection Engineer question set
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