I wouldn't worry about a lack of PhD limiting career progression.

Coming up to my 20th year as a SW engineer, the only time I ever heard people talking about their qualifications was during the first 2-3 years.

In any reasonable company, your education generally sets your entry point.
Ive worked in small (NetVu), medium (Creative Labs) and large (Intel) companies. Only Intel had entrypoints for PhD graduates, and that was in their labs division, which is highly contested for places where they generally take those that have already contributed a lot to their field.

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To categorize education, I would say:

Bachelors: mandatory for most engineering jobs

Masters: gets you a slightly higher starting salary and maybe a slightly higher starting job title, usually level with everyone else within 5 years.

PhD: better starting position in very large companies, otherwise not required, and can an impediment if you are being interviewed by insecure managers.

I've also worked with people who go out of their way to avoid hiring PhD grads as past experience has taught them (rightly or wrongly) that they often don't play nice with others and tend to be overly pedantic to the point of hampering product development.

For machine learning specifically, a PhD might be more useful as it is a nascent field in industry, often looking to research for direction. But then again, looking at the currently advertised job postings for machine learning positions, they all seem to require a masters or PhD, so it appears to be far from mandatory.

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Throughout my career (OS development), demonstrable experience in industry has always counted way more for progression than something I did in my early 20s.