I run several software teams that all have ML engineers on them, and two ML-specific teams of all ML engineers. Perhaps 14 ML engineers in total across my teams. We pay FAMGAN market rate. Their experience ranges from senior ML (6-8 years exp) to three who I hired right out of undergrad/masters, and two of those were interns who got job offers after internships.
Only one out of all of those have a PhD, and it’s only pseudo related. You absolutely do not need to have a PhD for 80% of positions in the modern world of ML in my experience, and I’d go as far as saying unless you want something significantly more prestigious than “market rate ML job doing interesting work” then a PhD is probably a net negative in life as an ML person given the opportunity cost. I have definitely turned down prestigious academia PhD types who wanted to move to industry in strong favor of strong SWEs with practical ML experience, and have a strong preference for same.
This definitely isn’t the answer academia or most people who have sunk cost of their time into PhDs would agree with, or necessarily like, but from a practical perspective it’s my experience across much of industry.
I’m still very early in my career, but I went straight from my bachelors to working in ML Engineering at a startup. I think it depends what type of ML job. If it’s heavy in research, I imagine a PhD would be much more important. We’re doing some research in terms of building some new models, but good portion of my job is on the infrastructure, pipelining, side of things.
It will be interesting if you can share how you landed in your first ML job, once you learnt the algorithms. I think getting the first job in ML role, if you don't have formal qualification in the related field is the hard part.
Got my foot in the door doing an after-school program teaching kids to code.
I leveraged that to get a teaching assistant job at a bootcamp for adult professionals.
I networked my arse off at the teaching assistant job until experienced programmers (such as instructors) realized I knew my stuff but was underemployed. I got a couple of side gigs doing BI Analytics that way.
After doing this, I had a tough set of interviews for my first full-time role. Every failed interview taught me about my weaknesses and blindspots, and I learned from them. I opted to get stronger at system design, stats & ML algorithms, though I feel like grinding leetcode could have been another approach at this point.
Because I had a wide set of marketable skills within data-oriented work, an analytics consulting firm took a liking to me. I had versatility for billable projects, and I got a bunch of tech certifications in AWS/etc. This role would be describable as 'Analytics Engineering'.
They overworked me for a little while, then my next role was a Data Scientist role that was on my own terms.
I don't want to make it sound like I could just jump in no problemo. I had to think strategically about how to climb each rung of the ladder. But I am now at a point where I have the experience needed to be a senior. While some companies might turn me down for not having a piece of paper, there are enough who actively want me that I am sitting pretty with my career.
I got a job as a junior ml dev at Coveo's R&D (through HackerNews no less) with an MSc in Experimental Medicine (BSc in Biochem before that). I was sure they'd never take me but they did. I think the main reason I got hired was that I did really well on their interview take-home test. I had a great time their but ironically, I left to start a PhD in deep learning as applied to biology so that I can strengthen my theoretical skills. No regrets, but I'm sure I'd have been able to grow at Coveo without the PhD.
Depends what you mean by ML; there are a lot of successful ML practitioners with no formal ML education, but far fewer (but not zero!) ML researchers without the expected academic background.
Finding a small, contained use for ML in your software/data job is a good path into the former, but I have no advice on the latter.
The attitude that you need a PhD to practice ML is over a half-decade old.
Modern ML tooling has progressed enough that not only is a PhD not necessary, but overcomplicating ML model construction fully utilizing said PhD can easily lead to technical debt and make things worse.
There's no mention of it because the article seems to be focused on research, not necessarily on applied work (for which you don't necessarily need a PhD).
