If you're gunning for ML research, that will be tough.

ML engineer, though? Don't overthink it--just find opportunities that you find interesting and apply. Yes, every company is of course going to prefer unicorn candidates who have "been there, done that", but the need for ML engineers has exploded (demand > supply), so they can't be so choosey.

And, in reality, for most companies hiring ML engineers, what they really need, first and foremost, is people who will be excited doing SWE work on ML pipelines...which is really another way to say SWE work (which happens to involve ML). So if you're a strong software engineer, a lot of places will immediately be interested in considering you for such a role.

(To be clear, I don't say the above to "knock" ML engineering, companies doing such work, etc.--rather just commenting that the reality is that at many companies shipping "real" ML products to production, the day-to-day pains are often about data pipelines breaking, scaling problems, software version incompatibilities, etc.

I.e., it isn't about exclusively solving deep Pytorch voodoo or similar.

Rather, much of it is "classic" SWE concerns that touch ML systems.

This is a good thing for you, as they know that they can hire someone smart and you can be productive pretty quickly--and then you can learn the more obnoxious ins-and-outs of paper implementation / why CUDA hates you / etc. as you grow.

Good luck!)