ML Careers Without PhD
Discussions center on transitioning into machine learning roles like ML engineering or data science from software engineering backgrounds without a PhD, including self-study paths, job market realities, and alternatives like data engineering.
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What about "data science" or applied machine learning? Or even ML research?
Can I suggest a longer, but (I think) better route?Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer.There are a lot of people in the “pure” ML space, people with science background, with phDs, etc.But there’s not enough people to support them: taking their models to producing, building their pipelines, etc.If you get into Data/ML engineer, you’ll
Funny there's no mention of ML without a PHD. Anyone done that?
Maybe you can find an application for ML at your current employer. Gain some experience without starting from the bottom.
Like everyone else is saying, from what I understand, going into ML as an ML expert only really makes sense if you have a PhD or at least a master's in the field from a well-known school. On the other hand there seem to be lots of ML companies who need more general software developers.
Learn llms (book, course, project, etc), practice leetcode, apply for ml engineer jobs. Your background puts you ahead of 99% applicants for those positions.
Are PhD degrees required to do machine learning research? Or can someone be completely self-taught and contribute to the field as much as those with PhDs?
I'd guess they have a very high drop-out rate.Regardless, "machine learning" is a very broad field and honestly I have no idea what an "ML engineer" is doing if they are one. It can cover any of the following:1. Cutting-edge academic research (do better on this test set)2. Doing data analysis to identify prediction ability3. Creatively thinking of useful features to evaluate.4. Implementing data pipelines/logging to obtain the features needed for #3.
How would one self-study enough about ML to be able to move into an ML engineering role without an academic background on it? Any recommended paths out there?
I finished my PhD a couple years ago, and my PhD friends that transitioned to software went into ML. Teach yourself some tool like Pytorch and some basic CS skills. ML’s barrier to entry is a PhD, but it doesn’t particularly matter which field it’s in so long as you can sell it right and pass the technical interviews.