Interview scorecard template

Lightfield Telepresence Solutions Developer interview scorecard

Evaluate Lightfield Telepresence Solutions Developer candidates across 4 weighted areas: technical depth, work that shipped, diagnosis under uncertainty, and working across the org. Technical depth leads at 35%, so probe depth in plenoptic capture and multi-view rendering: camera array calibration, depth fusion, view interpolation, GPU shader work, and display stacks like Looking. Use the rubric to compare role-specific evidence consistently.

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engineering applied sciencelightfieldreal time renderingtelepresencevolumetric capture
TL;DR
For technical depth, look for evidence the candidate explains angular versus spatial resolution trade-offs, quilt generation, calibration drift and depth estimation errors in their own implementation terms. For work that shipped, look for evidence the candidate names deployed systems with measured latency and framerate figures, plus the compromises made to hit them under real network conditions. 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

Probe depth in plenoptic capture and multi-view rendering: camera array calibration, depth fusion, view interpolation, GPU shader work, and display stacks like Looking Glass or Starline-style lenticular optics.

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 angular versus spatial resolution trade-offs, quilt generation, calibration drift and depth estimation errors in their own implementation terms.

02
Evaluation factor

Work that shipped

30% weight

Ask which telepresence systems they took from prototype to running installation: frame rates achieved, end-to-end latency in milliseconds, codec or WebRTC transport choices, and number of concurrent sites.

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 deployed systems with measured latency and framerate figures, plus the compromises made to hit them under real network conditions.

03
Evaluation factor

Diagnosis under uncertainty

20% weight

Test how they isolate faults spanning optics, sensors and code: ghosting, crosstalk between views, colour mismatch across cameras, sync jitter, or bandwidth collapse mid-call.

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 specific artefact hunt, using test patterns, per-camera logs and bisection to separate optical causes from pipeline bugs.

04
Evaluation factor

Working across the org

15% weight

Look for evidence of work alongside optical engineers, hardware integrators, UX researchers and clients running rooms; ask how they scoped installs and handled on-site commissioning.

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 handoffs with optics and hardware teams, and translating perceived image quality complaints into measurable engineering targets.

Evidence-led prompts

Interview questions for a Lightfield Telepresence Solutions Developer

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

  1. 01

    Can you describe your experience building telepresence systems using this technology?

  2. 02

    Which lightfield hardware or capture platforms have you worked with?

  3. 03

    Can you discuss a challenging project in this area and how you handled it?

  4. 04

    What experience do you have with 3D graphics and computer vision here?

  5. 05

    Can you explain algorithms or techniques you have implemented for lightfield data?

See the complete Lightfield Telepresence Solutions Developer question set
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