I think most of the comments assume that machine learning engineering equals machine learning. This is always not true. If you are an MLE, chances are that you work on the infrastructure and data pipeline side of things rather than on the research. If you are lucky, you get to productionize a prototype, which might involve re-writing it, or you might get to build an in-house hugging face. You absolutely do not require a phd to do this.

Then there are these other roles which involve prototyping new ways to train a model, or taking a paper from 5 months ago and see if it works for your use case. Or you know, just work on something that you can eventually publish. At FAANG, these are usually the "Research Scientist" or "Applied Scientist" roles. Most of these require a phd, but it's completely possible to get an offer with just a masters (I did), and I know of at least one case where the person "only" had a bachelors (and some experience). But by far the most straight-forward way to break into these roles is to have a phd.