Interview scorecard template

High-Performance Computing Specialist interview scorecard

Evaluate High-Performance Computing Specialist 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 parallel programming depth: MPI, OpenMP, GPU kernels, memory hierarchy, and where their code actually spends time. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate deep command of parallel models and memory hierarchy, evidenced by profiling their own code rather than guessing. For systems and trade-offs, look for evidence the candidate reasons clearly about scaling limits and cost per result, and names the trade-off they chose. 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 parallel programming depth: MPI, OpenMP, GPU kernels, memory hierarchy, and where their code actually spends time.

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

Deep command of parallel models and memory hierarchy, evidenced by profiling their own code rather than guessing.

02
Evaluation factor

Systems and trade-offs

25% weight

Test how they reason about scaling, interconnect limits, and cost per result across a real cluster.

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 clearly about scaling limits and cost per result, and names the trade-off they chose.

03
Evaluation factor

Evidence and rigour

25% weight

Check whether they benchmark honestly: reproducible runs, correct baselines, and speedups that survive scrutiny.

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

Benchmarks reproducibly against honest baselines, and can name a speedup claim they had to walk back.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they support researchers or engineers who are not HPC specialists but depend on the cluster.

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

Supports non-specialist users effectively and documents so the cluster survives their absence.

Evidence-led prompts

Interview questions for a High-Performance Computing Specialist

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

  1. 01

    Walk me through your experience with parallel computing: which models you have used, and where each one stopped scaling.

  2. 02

    Explain the MPI process to someone who has only ever written serial code, in about a minute.

  3. 03

    Which hardware acceleration tools have you used, and what did you change in the code to keep the GPU busy?

  4. 04

    Which programming languages are you proficient in for HPC work, and which would you reach for on a new solver?

  5. 05

    Tell me about your experience with job scheduling tools like Slurm, PBS Pro, or Grid Engine.

See the complete High-Performance Computing Specialist question set
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