My totally subjective view is that part of the definition of a PhD is gaining expertise in a very narrow field of study. I have had multiple people explain to me that the goal is to become the “world’s expert” on a really specific topic by the time you graduate. What that means is that you aren’t getting a PhD to gain generalist knowledge about machine learning, you’re going to learn to do research and to refine the way you tackle hard problems.

I guess what I’m really trying to say is that, unless you study some specific topic that is exactly what some company happens to be researching at the time you graduate, which may be unlikely, you are going to have to pick up a new (adjacent) topic within ML at some point. Even if you fall into the former category of lucky people, the thing you have a lot of expertise in might fall out of favor in ten years, or the company you work for decides to stop investing time in an approach about which you’re really knowledgeable, so you have to pivot to stay relevant/stay at the company. This is part of what you learn to do (in my experience and others’ that I’ve spoken with) in a PhD.

The other thing to consider is that it’s often easier to learn to be more applied given a theoretical background than the opposite.

Finally, it depends on what kind of job you want. Do you want to do research in industry? My advice is to not let the internet convince you that a MS is just as good as a PhD when it comes to getting such a job. In my experience, the people I’ve known who land research jobs in industry with less than a PhD are exceptional cases.

EDIT: I wanted to add that my personal theory is that there is only one really good reason to do a PhD, which is that you feel like you need to know more about your research area. It’s impossible to say whether a PhD will result in a monetary benefit or even a better job than you would’ve had. At the end of the day, you should do it because there’s something inside you that says you need to because the material benefits are not guaranteed.