[Mougatine]

You don't need a PhD for DS/ML Engineer. Even at FAANG, even in their research labs.

Usually a PhD is only in the requirements for Research Scientist (RS).

That said, I did a PhD (and am now a RS). It's a fantastic opportunity to learn fully focused during a few years.

But if your sole objective is the career (which is ok!), don't do a PhD. There are much easier ways to break into ML industry.

[joshvm]

To add to this, companies at Google-scale tend to have a huge variety of ML related jobs, ranging from low level things like optimising libraries for different hardware, to the more general research positions where people are working on their own pet projects. Plus everything in between - data management and curation for training models that get used in production, people who try and figure out how to productionise cutting edge research, people who build the infrastructure that other ML engineers use (and here again, everything from hardware/server people, cloud, site reliability, tooling) and the list goes on.

I know of at least one person who got an ML job at Google, but didn't apply specifically for it. They had a very strong ML background and applied for a generic software engineering and got team matched. That seems like a reasonable way to go if you don't want to go through a research interview loop.

[dekhn]

I;'d like to echo this. I learned a long time ago that I don't want to be a "machine learning engineer"- I have no interest in designing new networks, feature selection, or training as a daily job. I know how to do all those things but it's not somethign I pursued at Google. Instead, I found jobs where I could work with those people (often the ones doing the real state of the art research at scale) using my experience, in ML, data engineering, pipelines, and HPC.

There is nothing quite like having a world-class researcher ask you to figure out why their model is exploding, and tracking down the crazy things that happen on TPUs when their math isn't absolutely perfect, then helping them fix it, and see them publish their results (or put them in prod). Or knowing enough software and hardware to debug a tensorflow TPU problem with an oscilloscope connected to the voltage regulator in a hardware lab.

Personally, i gained these skills over a long period starting in the mid-90s (working on machiine learning, and then later HPC for biology, and ultimately back to machine learning). But I am a slow learner. probably the shortest path is to get accepted to a major university and do really well in your ML and CS classes, then parlay that into a job in a FAAMG, then figure out what you want to do with all your skillz.

[pvarangot]

I got a unicorn senior RS offer without a PhD from a company that had mostly former FAANG top brass after interviewing without knowing it was for RS, I thought it was DS/ML. I declined because of "culture fit". Everyone has a PhD, they assumed I had one, I don't even have a bachelors. We still hang out and laugh about it.

I had been working in Attitude Determination and Control and Optical Systems Engineering for seven years before that interview and I just like, knew the stuff from the job. I've been back on pure-SWE roles for four years already and I don't think I could do it now. I have the intuition but I couldn't white board proofs for tree based algos and manipulate integrals like I did on that interview for sure.