Evaluate Simulation Designer candidates across 4 weighted areas: technical depth, work that shipped, diagnosis under uncertainty, and working across the org. Technical depth leads at 35%, so check depth in physics and behaviour modelling: equations of motion, six-degree-of-freedom dynamics, MATLAB/Simulink or Python models, real-time loop rates, DIS or HLA interoperability standards. Use the rubric to compare role-specific evidence consistently.
engineering applied sciencemodel validationreal time systemsscenario designsimulation modelling
TL;DR
For technical depth, look for evidence the candidate explains fidelity trade-offs quantitatively, names solver types and update rates used, and links model choices to validated training or test objectives. For work that shipped, look for evidence the candidate describes shipped simulators with users, delivery dates, acceptance criteria met, and specific scenarios they authored rather than generic project involvement.
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 depth
35% weight
Check depth in physics and behaviour modelling: equations of motion, six-degree-of-freedom dynamics, MATLAB/Simulink or Python models, real-time loop rates, DIS or HLA interoperability standards.
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
Explains fidelity trade-offs quantitatively, names solver types and update rates used, and links model choices to validated training or test objectives.
02
Evaluation factor
Work that shipped
30% weight
Ask which simulators, scenarios or synthetic environments they delivered end to end: Unreal or Unity builds, instructor operator stations, terrain databases, hardware-in-the-loop rigs, acceptance test results.
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
Describes shipped simulators with users, delivery dates, acceptance criteria met, and specific scenarios they authored rather than generic project involvement.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Probe how they diagnose divergence between simulation output and reference data: frame drops, integration instability, latency spikes, sensor model artefacts, missing tuning data from subject matter experts.
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 concrete fidelity or performance defect, the instrumentation and logs used, and the validation evidence that confirmed the fix.
04
Evaluation factor
Working across the org
15% weight
Assess work with instructors, pilots, clinicians or test engineers: requirements capture from subject matter experts, verification and validation reviews, handover documentation and trainee feedback loops.
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
Cites named stakeholder groups, how their feedback changed scenario design, and how requirements traceability was maintained through reviews.
Evidence-led prompts
Interview questions for a Simulation Designer
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
What simulation software are you proficient in, and how have you used it?
02
Can you describe a challenging simulation project and how you overcame the difficulties?
03
Have you integrated a simulation with other systems or technologies?
04
What methods do you use to validate and verify your simulations?
05
How do you ensure accuracy and realism in your simulations?
