Data Engineer is the outsourced part of what no ML researcher wants to do - a thankless, high-pressure, dead-end job which in no way leads to actually doing ML later - it would pigeon-hole the OP as unfit for real ML.
The best way is to take Stanford Deep Learning courses at SCPD, build a reputation, do real ML work (even if it's not a PhD, it's the same courses Stanford PhDs take).
I'm struggling to find a single resource that ELI5 step-by-step how a neural network does digit classification (learns 0-9) in a grid of 3x5, like the traffic signal countdown timer
I agree with your first sentence. I'm not sure I would recommend SCPD.
If you want to do real ML work, you pretty much need the PhD. This is a hard thing for people who have 140+ IQs but do poorly for whatever reason with formal education to accept, but it's true. Even if you get one real ML job without a doctoral degree, you won't get a second one.
Sure, other 140+ IQs can recognize very smart people with only (or not even) a bachelor's degree, but (a) your career in industry will be influenced by the opinions of not-smart but politically empowered people who rely on heuristics like educational prestige because they can't judge the genuine article, and (b) some of those 140+ are nevertheless scumbags and will use (a) against you.
If you want to be a serious player in an academic field like ML, you need to not only get the degree but start publishing and never stop. It doesn't matter all that much if your papers are any good; no one in industry will ever read them. But you need the image of a successful academic who's just slumming it and can go back any time.
I've worked in industry doing what I would consider "real ML" (i.e. shipping models that are core, revenue generating product features into production) for a decade at a range of companies from startups to fortune 500 companies and the part about needing a PhD is entirely nonsense (as is the majority of the content in this comment).
Maybe if you consider doing "real ML" exclusively working with Deep Mind or on Meta core research team, but there's a lot more to "real ML" than just these teams.
I'm curious how long you've been in industry and what types of orgs you've worked at to get this impression?
edit: In general the comments in this post are bizarrely out of touch with reality and have really shifted my perception of the avg HN commenter.
So basically no chance for the OP to ever get to ML as getting into a top 10 ML school for a PhD is a minor miracle, finishing it even bigger and that's just the initial qualification step?
Academia remains an option even if you don't get into a top-10 ML school, if your research is good. Granted, it's tougher to get published and cited, because you're less likely to know people who can push your work, but if you do good work, you can still play.
Government may be an option, although it doesn't pay as well as industry and you can end up in a comfortable but stifling role.
In corporate, though? Yeah, you pretty much need to have the appearance of star power, which means degree prestige matters. Whether you're actually any good (and, trust me, there are plenty of mediocre people from top schools) doesn't really matter, because the decision-makers are too stupid to know the difference.
There are ways to play this, though, if you're aiming at industry. Harvard isn't a top-10 CS department, but the people in corporate aren't going to know that, and so "Harvard PhD" is going to make them fellate you just because it's Harvard. That may be an avenue. Or, better yet, get a PhD in something that sounds technical but is easier and less selective.
That said, if your goal is to play the corporate game and make a lot of money, you should probably forget about ML and focus on becoming a manager as quick as possible. If your goal is to do intellectually stimulating work, you should probably not consider corporate, because your work is going to be evaluated by people who are literally 50 IQ points too dumb to do so, and while this noise factor is manipulable rather than truly random, the people who have the skills to do perform said manipulation tend to go into management, not technology.
I don't think what you consider "real ML work" is what OP is asking for. While they wrote "research", the three points they wrote at the end is not research, and I don't think they want to be an academic and/or write any papers.
The best way is to take Stanford Deep Learning courses at SCPD, build a reputation, do real ML work (even if it's not a PhD, it's the same courses Stanford PhDs take).