I wrote scientific simulation software in academia for a few years. None of us writing the software had any formal software engineering training above what we’d pieced together ourselves from statistics courses. We wrote our simulations to run independently on many nodes and aggregated the results at the end, no use of any HPC features other than “run these 100 scripts on a node each please, thank you slurm”. That approach worked very well for our problem.

I’d bet a significant part of compute work on HPC clusters in academia works the same way. The only thing we paid attention to was number of cores on the node and preferring node local storage over the shared volumes for caching. No MPI.

There are of course problems requiring “genuine” HPC clusters but ours could have run on any pile of workers with a job queue.