Why pre-screen High-Performance Computing Specialists before the technical panel and code walkthrough
Pre-screening HPC specialists protects your panel's time because the applicant pool splits three ways: computational science PhDs, Linux cluster administrators, and cloud infrastructure engineers, and all three list CUDA, Slurm, and MPI on a resume. A resume cannot show whether they profiled a code or simply recompiled it with different optimisation flags. A ten minute screen surfaces node counts, baselines, the profiler they actually opened, and how they explain a bottleneck to a researcher.
What actually matters when screening High-Performance Computing Specialist candidates
- 01
Technical proficiency
Probe parallel programming depth: MPI, OpenMP, GPU kernels, memory hierarchy, and where their code actually spends time.
- 02
Systems and trade-offs
Test how they reason about scaling, interconnect limits, and cost per result across a real cluster.
- 03
Evidence and rigour
Check whether they benchmark honestly: reproducible runs, correct baselines, and speedups that survive scrutiny.
- 04
Collaboration and communication
Assess how they support researchers or engineers who are not HPC specialists but depend on the cluster.
Pre-screening questions to ask High-Performance Computing Specialist candidates
12 questions grouped by what they test. Ask the same set in every screen and score answers on a consistent scale, or send them as an async video screen and compare answers side by side.
Parallel programming depth
4 questions01Walk me through your experience with parallel computing: which models you have used, and where each one stopped scaling.
Listen forSpecific codes with rank and thread counts, node counts, and a clear account of where strong or weak scaling flattened and why.
Parallelism described only in coursework terms, with no cluster, no rank counts, and no problem they personally scaled.
02Explain the MPI process to someone who has only ever written serial code, in about a minute.
Listen forRanks and communicators, point-to-point versus collectives, blocking versus non-blocking, and a concrete example such as halo exchange or a global reduction.
Recites textbook definitions of MPI_Send and MPI_Recv but cannot say why collectives beat a loop of sends.
03Which hardware acceleration tools have you used, and what did you change in the code to keep the GPU busy?
Listen forCUDA, HIP, OpenACC or SYCL plus libraries like cuBLAS or cuFFT, with talk of occupancy, coalesced access, and removing host to device transfers.
Lists CUDA on the resume but cannot describe a kernel they wrote or how they measured transfer overhead.
04Which programming languages are you proficient in for HPC work, and which would you reach for on a new solver?
Listen forFortran, C, or modern C++ for kernels with Python and shell for orchestration, plus a reason tied to compilers, vectorisation, or existing libraries.
Python only, treats NumPy as the whole story, or dismisses the Fortran and C++ codebases they would inherit.
Cluster systems and scaling
3 questions05Tell me about your experience with job scheduling tools like Slurm, PBS Pro, or Grid Engine.
Listen forWriting sbatch scripts, job arrays, gres and GPU requests, partitions, fairshare and QOS, and diagnosing why a job sits in pending.
Only ever ran job scripts someone else wrote and cannot explain what determines queue position.
06What issues have you hit with HPC storage and parallel file systems?
Listen forLustre or GPFS metadata contention, small-file penalties, stripe counts, collective MPI-IO or HDF5 writes, quotas, and purge policies.
Treats the parallel file system as a big disk and blames slow I/O without measuring or changing striping.
07What is your experience with cloud-based HPC, and how did the cost per result compare to on-prem?
Listen forNamed setups such as AWS ParallelCluster, Azure CycleCloud, or EFA and InfiniBand instances, with node hour, egress, and spot interruption costs.
Assumes cloud nodes behave like on-prem hardware and ignores interconnect quality, egress charges, and preemption.
Benchmarking and rigour
3 questions08Record a walkthrough of a project where you improved system performance: the baseline, what you changed, and the measured speedup.
Listen forA defined optimised baseline, named profiler output, before and after timings, node hours saved, and honesty about the remaining bottleneck.
A large speedup with no stated baseline, no profiler, or a comparison against deliberately unoptimised code.
09What methodology do you follow for HPC performance evaluation and tuning?
Listen forProfile first, roofline or arithmetic intensity analysis, strong and weak scaling studies, repeated runs with reported variance, and fixed node placement.
Tuning by guessing at compiler flags, one timing run, no variance reported, and no scaling study.
10Which debugging tools and techniques do you use when a parallel job fails at scale?
Listen forTools such as gdb, DDT or TotalView, Valgrind or AddressSanitizer, Intel VTune, Nsight Compute, plus a method for reproducing races and deadlocks.
Print statements only, with no tool for memory errors, deadlocks, or race conditions across ranks.
Support and availability
2 questions11Tell me about working in a team on an HPC system: how did you support users who were not HPC specialists?
Listen forConcrete support work: office hours, module files, example job scripts, refactoring a researcher's code, and translating profiler findings into plain guidance.
Blames users for their own jobs, points at documentation, and offers no example of teaching or fixing someone else's code.
12How have you kept HPC systems reliable and available in previous roles, including maintenance and on-call?
Listen forMonitoring with Prometheus and Grafana or Nagios, node health checks, draining and rebooting nodes, planned maintenance windows, and an on-call rotation they took part in.
No monitoring specifics, no maintenance planning, and uptime treated as somebody else's responsibility.
How to score responses
Score every candidate on the same four criteria immediately after the screen. At this stage you are shortlisting for panel interviews, not making the final call.
Technical proficiency
35%5Deep command of parallel models and memory hierarchy, evidenced by profiling their own code rather than guessing.
Systems and trade-offs
25%5Reasons clearly about scaling limits and cost per result, and names the trade-off they chose.
Evidence and rigour
25%5Benchmarks reproducibly against honest baselines, and can name a speedup claim they had to walk back.
Collaboration and communication
15%5Supports non-specialist users effectively and documents so the cluster survives their absence.
Async video lets you watch an HPC candidate narrate a roofline chart or profiler trace on their own screen, which is where you hear whether the speedup story holds up and whether a researcher could follow the explanation.
Try it on HirevireScreening FAQ
Process basics
How long should a pre-screening round for an HPC specialist take?
Ten to fifteen minutes of recorded answers is enough. Two audio questions on parallel programming and storage, one video walkthrough of a performance win, and short text answers on languages, schedulers, and debugging tools give you the signal. Save cluster access, code review, and roofline discussion for the technical panel, where a whiteboard and real hardware are available.
What should you ask a candidate whose only HPC experience is an academic cluster?
Ask about scale and scheduling rather than production operations. Request node counts, rank and thread counts, the partition and QOS they submitted under, and what they did when a job sat pending. Academic users who wrote their own sbatch scripts, tuned Lustre stripe counts, or ran scaling studies often transfer well into research computing support roles.
Evaluating answers
How can you tell whether a candidate's reported speedup is honest?
Ask what the baseline was. Credible candidates name an optimised serial or single-node build, state compiler and flags, report multiple runs with variance, and fix node placement between runs. Speedups measured against a debug build, a single timing, or a different problem size are not comparable, and candidates who cannot describe the baseline usually did not control it.
Does an HPC specialist still need Fortran and C++, or is Python enough?
Python alone is rarely enough for the codebases they will inherit. Most production solvers in CFD, climate, and quantum chemistry are Fortran or C++ with MPI, and the work involves reading and modifying them. Python and shell for orchestration and analysis are expected, but listen for comfort with compiled languages, compiler behaviour, and vectorisation.
Go deeper on this role
Sanat has been hiring since 2012 and watching the recruitment industry change up close ever since, and turned that screening process into Hirevire's video screening platform. LinkedIn
