Not OP but I deal with this a lot. In my experience a lot of folks working in mainstream ML haven't been exposed to it unless they specifically focused on it. It might just be a course load thing... getting the most out of these probabilistic PLs requires fairly deep expertise in both probability theory/Bayesian stats as well as in CS and you have a finite amount of courses you can take in school. Plus, a lot of the work in this area pre-dates the modern focus on deep learning or machine learning in general, so a lot of the knowledge tends to be held by professors/researchers that may not be as involved with the "new" ML courses. And of course, Math/Stats/CS departments don't always play nicely with each other and like to fight turf wars, though I've noticed cross-disciplinary research among the three becoming more accepted at the universities/institutes I work with.

As a case study, I did most of my grad work on solving Bayesian inverse problems using probabilistic programming for applications in engineering, which is pretty cross-disciplinary. I now work mostly in ML, but I didn't really even touch anything in the ML domain until after I finished school. I could have, the courses were available, but they just weren't relevant to me at the time.