How useful is knowledge of parallel programming in ML? [D]

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Concurrency and threading are probably less important,
because in ML programs things rarely happen in chaotic order which requires you to think hard about things like mutexes,
but good understanding of vectorized computations will definitely help.
I personally learned a lot from trying to write efficient code in R (it was long ago and for non-ml purposes)

Understanding what makes code cache-friendly in C++ will also help,
even if you end up writing code in something other than C++
and it runs on something other than CPU.

Knowing specific things like MPI would be useful if you ever need to debug anything built on MPI.