Speaking as faculty myself, the only job advice that you should listen to from an academic is how to get a job in academia. Even the ones that had a job in industry prior to their academic job have a reason for preferring their academic job. Their firsthand knowledge of the industry is stale at best, and their current information is all hearsay.

If you want to get a job in industry, you need to get advice from alumni that have (recently) gotten a job in the industry you are interested in.

The newly hired faculty at my school have mostly been ML/AI people. While it seems completely wrong that ML is needed to break into industry, is it true that research roles strongly favor ML right now?

I'd say that with some caveats, a freshly minted PhD in CS/stat/math/etc. with an ML dissertation will have an easy time finding an academic job. The caveats are that you might not get the job you really want. You might have to do a post doc first. Even if you don't have to do a post doc, you might not be at the most prestigious university, might not start out as tenure track, and might not even be in a CS/stat/math/etc. department.

You could end up at a medical school or a business school, a ton of mediocre PhD's take these jobs because Stanford and MIT never called them back. The medical and business schools think they hit the jackpot- they know they got someone mediocre, but at least they found someone. The mediocre PhDs usually end up happy because they found a job and medical and business schools usually pay well.

I'd say that a lot of the jobs that favor ML are from schools/departments that are playing catch up and trying to cash in because they saw a wave of grant money going to ML research. They are either smaller and less prestigious, or interested in the application to their field, rather than any direct interest in ML itself.

An ML focus probably isn't the boost you may have thought if you want a job at Stanford. The big schools have their pick of the litter and are already flush with ML talent, so being "an ML guy" isn't a magic serum that hides all your other flaws. You still have to be smart enough and positioned well (good school, good advisor) to get a job at Stanford.

I am in such a situation now. Soon to graduate with a PhD in ML about Bayesian networks, and do not know what to do in future.

Originally I learned programming, so I can write classical software, working from home. Never wanted to do anything else, but that does not pay. Only for AI research I could get funding

I don't think that's true. The vast majority of software development jobs require no ML at all. I don't see that changing in the next 10 to 20 years.

Even further the majority of work in ml driven products is not related to the ML model.

this 1000%.

I don't think it is true either. But my friend tells me that multiple profs tell him that he needs to learn ML.

I learned ML in school 13 years ago. I've been happily employed since then at a major tech company with increasing responsibility and the only time I've really needed that background was to call BS on a given project needing a custom ML component.

Maybe those profs are just huge fans of ML–you know, the original ML, i.e., Standard ML, OCaml, etc.

“Who needs a neural net? OCaml can already match patterns!”

That's ridiculous. There are tons of jobs in everything from devops to pure frontend to writing shit in COBOL.

ML is sexy and pays a lot, sure, but it's far from the only thing going.