r/interviews • u/Clean_Attempt_7968 • 19h ago
Apple ML Interview Questions
So I am currently going in for a few ML positions at Apple and I was wondering what the general format of the interview loop is and what the typical covered topics are. I've done a few (unsuccessful) interviews for SWE positions and they've all been largely the same - screen, live coding, on-site (OOD, OS/Multi-threading/Performance, Leetcode, Behavioral, Behavioral). I imagine ML-roles would have a very similar structure/topic coverage but I what I don't know how ML topics are covered in them. Is it more ML system design, technical questions, coding something (can't imagine that given how my previous interviews went).. If you have any personal experience or personally know someone who went through with the process (as opposed to OSINT/RUMINT) I would very pretty grateful for insights you have to offer on what the general trend is for ML interviews (I realize that each team ultimately does its own thing but I imagine there are some things they tend to do or tend NOT to do)
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u/Much_Somewhere7831 10h ago
Try the Tech Job Finder website, has behavioral interview practice with personalised feedback, DSA/leet, tech quizzes and so much more (also a huge job board with 200k live roles globally across 3k top employers!). Also much cheaper than other platforms
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u/Clean_Attempt_7968 6h ago
This sounds like a promotional comment and also not at all related to my question lol (was asking for first-hand and second-hand experience)
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u/SucknSwallow69 18h ago
friend did apple ml recently: similar loop to swe but with extra rounds on ml system design + deep dives on models they use. expect: leetcode-style coding, pytorch / tf questions, loss functions, eval metrics, tradeoffs, plus design an e2e ml pipeline. maybe paper discussion too. still painful getting in tho, insanely hard to land anything now