The point is, as you pointed out, that you code against the appropriate level of abstraction. You write a ML workflow appropriate language like Python in something like C++/rust, and ML flows in Python. That should really not be that hard to understand.
I am not an expert, but from what I've seen, PyTorch is mostly a thin wrapper over the C++ libtorch. The same is true for DOM as well, but nobody uses DOM directly, whereas everybody uses PyTorch.
The big shift is Jupyter, but that's mainly for exploratory programming. If you already know what you're doing, there's no reason why C++ should be worse than Python for training. It's likely that most ML engineers do not have experience with C++.
I don't have that experience either but from what I've seen, C++ is very powerful, so once you subtract the jupyter, there's not really too much left.
BTW: You cannot use DOM/CSSOM from C++, the only API is JS, so your argument is theoretical.
