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haljm t1_jcazfgr wrote

Try more applied fields!

Not in industry (PhD student), but used to do deep learning type research with a computer vision background. I'm now working on applying ML for computer systems. At least 90% of my actual research work is building the system, collecting the data, and deciding how to frame the problem; for that last 10%, essentially anything reasonable will do. The catch is that if you don't have a solid ML background, you might completely miss that last 10% and never realize!

It's much more satisfying since if you're working on applications, you likely have potential downstream use cases lined up (that's what collaborators are for!). You're also probably the only person working on this specific problem formulation, so you don't need to worry about beating SOTA and squeezing out that last 5% -- your data-driven algorithm is 2x better than a traditional approach, so does it really matter?

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nopainnogain5 OP t1_jcc9his wrote

In case I'd like to dive into something along these lines, how such positions tend to be called?

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haljm t1_jccw6ef wrote

I'm probably not the most qualified since I'm a PhD student, but in my experience it's generally an ML position where the description requires you to have some domain knowledge or a position title in the application domain that specifies that you're doing ML.

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prettyyyyprettyygood t1_jce8ye3 wrote

I think "Machine Learning Engineer" or even just "Data Scientist". Most of the jobs out there are probably exactly like this. In fact there's a shortage of people who want to do the 'boring' stuff, compared to people who want to be researchers. If you are good at MLops and implementing solutions that you know will work, you're super valuable.

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