>or simply because we can abstract a lot of this away (ex: TensorFlow).
That would work up to the point a better abstraction tool/framework comes along. I'd never try to build a career on a single framework, because frameworks come and go.
Building a career around a framework is never a good idea. If you know your shit, it shouldn't matter what framework you're using.
Theano and TF, for example, both make similar abstractions: graphs and numerical functions on top of the same matrix library, even. I would suspect someone could move between the two fairly easily. The problem is that a programmer can use TF/Theano/etc.'s built-in gradient descent functions pulled from a tutorial with their data subbed in _instead_ of learning the details of backpropogation, end up with decent results, and claim to have a basic understanding of ML - when really, they've managed to avoid it almost completely.
I don't follow job postings for ML, but I will say this - there is inherent value in being familiar with certain frameworks when hiring for non-entry-level positions. This is particularly true for ML, where it can be very useful to know the optimizations that the libraries do/don't make behind the scenes, etc.
Should most ML job postings require n years of experience in some framework? Maybe not - but I can see why a company might see value in it.
I don't know where this meme comes from. Six out of the seven jobs/internships I've had (in SF, Seattle, and Toronto) didn't care about any specific framework or language whatsoever and still don't.
Build your career on whatever tool(s) companies want.
There's only in web development where the hype change every year. (And even there there are plenty of companies that are lagging enough behind to still have opportunities in the old thing).
That would work up to the point a better abstraction tool/framework comes along. I'd never try to build a career on a single framework, because frameworks come and go.