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 fluency with LRA, ERM, piezo and voice coil actuators: resonance tuning, waveform authoring in tools like Immersion or TouchSense, and psychophysical thresholds such as JND.
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
Names actuator classes with drive requirements, cites resonant frequency tuning, and explains perceptual thresholds behind chosen amplitude and duration values.
02
Evaluation factor
Work that shipped
30% weight
Ask for shipped devices carrying their haptic effects: phone taptics, automotive touch panels, VR controllers or surgical trainers, plus effect library size and latency budgets met.
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
Points to specific released products, describes their effect library and the sub-20ms latency and power targets the design had to satisfy.
03
Evaluation factor
Diagnosis under uncertainty
20% weight
Test how they debug a buzzy or mushy effect: distinguishing driver clipping, mechanical coupling loss, enclosure damping or firmware timing jitter using accelerometer measurement.
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 isolating cause with accelerometer traces and swap tests, separating mechanical mounting problems from driver waveform or scheduling faults.
04
Evaluation factor
Working across the org
15% weight
Check collaboration with mechanical, firmware and UX teams on mass, mounting stiffness and gesture mapping, plus running user studies to validate tactile discriminability.
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 negotiating mounting and mass constraints with ME, aligning cues with UI states, and feeding user study results back into effect design.
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