My experience from writing a lot of array-based code in NumPy/Matlab is that broadcasting absolutely has made it easier to write my code in those ecosystems. Axes of length 1 have often been in the right places already, or have been easy to insert. It's of course possible to create a big mess in any language; it seems likely that the NumPy code you saw could have been neater too.
In machine learning there can be many array dimensions floating around: batch-dims, sequence and/or channel-dims, weight matrices, and so on. It can be necessary to expand two or more dimensions, and/or line up dimensions quite carefully. Einops[1] has emerged from that community as a tool to succinctly express many operations that involve lots of array dimensions. You're likely to bump into more and more people who've used it, and again it seems there's some overlap with what Rank does. (And again, you'll see uses of Einops in the wild that are unnecessarily convoluted.)
[1] https://einops.rocks/ -- It works with all of the existing major array-based frameworks for Python (NumPy/PyTorch/Jax/etc), and the emerging array API standard for Python.
Named axis systems like einops are much better regarded. I've seen some implementations of similar functionality in APL, and various discussions of how it could be built into an array language (I have my own design where that's a core idea, but not using APL syntax).
Example:
A / A.sum(axis=3, keepdims=True)
A / sum(A, 4)
> a friend had written some code in—either numpy, or something else borrowing its model—and wanted to translate it to j. I could not figure out what she was trying to accomplish with the many seemingly-extraneous reshapes and transposes, until I found out what they did in numpy. The whole thing reduced to a single, trivial application of rank in j
[0] https://aplwiki.com/wiki/APL_Farm, main or "Array languages" channel