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 depth
35% weight
Probe depth in matching algorithms and standards: FAR/FRR threshold tuning, ISO 19794 and ANSI-378 template formats, liveness detection, and SDKs from Suprema, ZKTeco or Innovatrics.
Evidence to listen for
Explains the physics or mechanism behind their work, not just the tooling
Names the standards, tolerances, and constraints they designed against
Can defend a design decision under follow-up questions
Distinguishes what they personally engineered from what the team delivered
Five-point scoring guide
1
Poor
Cannot explain the fundamentals of their own stated specialism.
2
Needs Improvement
Knows the vocabulary but not the underlying mechanism; struggles under follow-ups.
3
Satisfactory
Solid working knowledge for the role; depth thins out on edge cases.
4
Very Good
Strong command of the domain; explains trade-offs and defends decisions well.
5
Excellent
Quotes real FAR/FRR operating points they set, explains template interoperability standards, and defends liveness or spoof-detection choices with test data.
02
Evaluation factor
Work that shipped
30% weight
Ask for deployed systems: headcount enrolled, terminal models, Wiegand or OSDP wiring to controllers, and how attendance data reached payroll or HRMS like SAP or Zoho.
Evidence to listen for
Names specific programmes, parts, or systems that reached production or field use
States their own scope inside the project
Can give measured outcomes: yield, cycle time, cost, failure rate
Explains what went wrong and what they changed
Five-point scoring guide
1
Poor
No delivered work; experience is coursework, lab-only, or purely observational.
2
Needs Improvement
Contributed to projects but cannot say what shipped or what their part was.
3
Satisfactory
Has delivered real work; outcomes described without numbers.
4
Very Good
Names shipped work and their scope, with some measured results.
5
Excellent
Names specific rollouts with enrolment volumes, device fleets, integration endpoints, and post-launch punch accuracy or shift reconciliation figures.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Test how they diagnose field failures: worn or wet fingerprints, backlit face capture, duplicate enrolments, clock drift across terminals, and buddy-punching disputes raised by HR.
Evidence to listen for
Describes a real failure they chased to root cause
Shows a method: isolate variables, reproduce, measure, eliminate
Distinguishes correlation from cause
Says what they ruled out and why, not only what the answer turned out to be
Five-point scoring guide
1
Poor
No diagnostic method; guesses or escalates immediately.
2
Needs Improvement
Trial and error with no structure; cannot explain how they narrowed the cause.
3
Satisfactory
Reasonable method on familiar problems; less structured on novel ones.
4
Very Good
Clear systematic approach with a real root-cause story.
5
Excellent
Walks through a real failed-match investigation, separating sensor, template quality, environment, and enrolment error before changing thresholds.
04
Evaluation factor
Working across the org
15% weight
Check how they work with HR, payroll, IT security and legal on consent, biometric data retention, GDPR or BIPA obligations, and union or workforce pushback.
Evidence to listen for
Explains technical constraints to non-technical stakeholders without condescension
Has negotiated scope, cost, or timeline with manufacturing, product, or suppliers
Documents decisions so others can act on them
Takes review feedback without defensiveness
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism; dismissive of other functions.
2
Needs Improvement
Communication gaps cause rework; avoids stakeholder contact.
3
Satisfactory
Works adequately with other teams; documentation is thin.
4
Very Good
Communicates clearly across functions; reliable collaborator.
5
Excellent
Describes concrete negotiations on retention policy and encryption at rest, plus how they handled employee objections to enrolment.
Put this rubric to work
Score every candidate against the same standard
Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.
