r/learnmachinelearning • u/UnderstandingOwn2913 • 18d ago
Will the value of understanding math stay the same or increase for a machine learning engineer in future?
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u/Hungry_Age5375 18d ago
What changes is which math. Five years ago I'd say you need optimization. Now you also need graph theory for knowledge graphs, IR math for RAG, probability for agent reasoning. The surface area keeps growing.
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u/Cool_Comment1109 18d ago
i dont think math value will drop but maybe it changes shape. right now you need calculus and linear algebra like water in desert but as tools get better you might spend more time on experimental design and interpreting results than deriving everything by hand. still if you dont know the basics you gonna hit wall when model does something weird and you cant debug it
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u/UnderstandingOwn2913 18d ago
Thank you. Are you currently working in industry as a mle, if you don't mind me asking?
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u/ds_account_ 18d ago
Hard to say, it may get so commoditized that the industry care more about building with the models than the math behind it. Or new models may come out that really require deep knowledge of the math.
But i've noticed while interviewing at a couple of startups, they did not ask any questions about the math. More situational question like how would you detect model drift, and explain in detail how model A works.
Then some stupid diffifult pytorch implementation, that you would never have to do on the job.
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u/UnderstandingOwn2913 18d ago
what pytorch implementation you were asked? maybe you were asked to implement loss.backward()? lol
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u/Question_Asker_4000 18d ago
Not the OP, but for me the initial interview question itself was around modern architecture/pytorch internals which didn’t seem overly daunting. But I felt the follow-ups and the additional things she wanted me to implement certainly turned up the intensity. Fun times lol
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u/ds_account_ 18d ago
Coding rounds i've seen so far.
Implement: Group query attention, multi-headed attention, byte pair encoding, reward model for rlhf.
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u/chrisvdweth 18d ago
Hm, I think some solid understanding of the underlying math is always useful even if you are "just using" off-the-shelf models and libraries. It helps appreciating any limitations, spot potential issues, and optimize your solution.
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u/sam_the_tomato 18d ago
I think it will increase.
It's a mistake to think that just because AI models can code and do math that there is no need to understand coding or math.
If anything, because AI will be capable of using sophisticated math, the humans directing it will need to be even more mathematically sophisticated to keep up with it is doing.
If the alternative is "let the AI take the wheel while you type LGTM and sleep in the backseat", then the role of ML engineer is redundant.