HPC Systems Architect
Posted Oct 2, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a hardware or software platform you evaluated and chose. How did you make the call?
What they are checking: Whether they run fair, data-driven evaluations instead of following vendor claims.
Tell me about a time you had to retire or replace an existing system. How did you manage the transition?
What they are checking: Experience managing the full lifecycle of infrastructure without disrupting users.
What would you do
Researchers complain that jobs are waiting too long in the queue. How do you investigate?
What they are checking: Whether they look at scheduler policy, utilization and demand before buying more hardware.
How would you set architectural standards that teams in different regions actually follow?
What they are checking: Whether they can lead through influence and partnership with global teams.
Leadership asks whether to build more on-prem capacity or use cloud for bursts. How do you frame the decision?
What they are checking: Whether they connect technical architecture to cost and long-term business goals.
About the work
How would you architect a GPU cluster for large machine learning training workloads from the ground up?
What they are checking: Real experience designing HPC for ML, including compute, interconnect, storage and scheduling.
How do you choose between InfiniBand and Ethernet-based fabrics for a new cluster?
What they are checking: Whether they can weigh performance, cost and operational complexity for the interconnect.
What monitoring would you put in place to catch performance bottlenecks before users notice?
What they are checking: Their approach to proactive monitoring and analytics across the HPC ecosystem.
How do you design storage so it doesn't become the bottleneck for training jobs?
What they are checking: Understanding of parallel file systems, caching and data loading for ML workloads.
How do you plan for power and cooling when GPU density keeps rising?
What they are checking: Practical awareness of data center limits that shape modern HPC designs.
What scheduler have you run at scale, and what did you have to change to make it fair?
What they are checking: Hands-on experience with tools like Slurm or Kubernetes for shared compute.
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Practice this out loudThe full job description
As published by IMC Trading.