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      Machine Learning PhD Intern Interview

      5 Dec 2023
      Anonymous interview candidate
      Zürich

      Other Machine Learning PhD Intern interview reviews for Apple

      Machine Learning PhD Intern Interview

      11 Mar 2014
      Anonymous interview candidate
      Austin, TX
      No offer
      Neutral experience
      No offer
      Positive experience
      Easy interview

      Application

      I interviewed at Apple (Zürich)

      Interview

      1 screening interview, 2 technical interviews. Fairly easy, the interviewers were very kind and helped through the process. The questions were related to my field of research and their project.

      Interview questions [1]

      Question 1

      Comment on different PyTorch code snippets and fill it in with the training loop code.
      Answer question
      Average interview

      Application

      I applied through university. The process took 6 weeks. I interviewed at Apple (Austin, TX)

      Interview

      Following a departmental email, I sent my details and CV to the person coordinating the hire. I Was contacted a few days later to let me know my application was in and that they'll schedule a phone screen in a few days, which they did (via email). The phone screen was fairly general and non-technical, "what type of work you've done in the past, what type of work do you prefer doing" type of questions. After the screen, I was contacted via email and an on-site half-day of interviews was scheduled (I live in driving distance of Apple offices). The interviews were half-technical (mostly data structures and algorithms questions, some general machine learning knowledge questions) and half-"touchy-feely" (what's the project you've done that you like the most, and why, why Apple, etc.). As of a week ago I've been told the hiring process is still ongoing and I am waiting for their final word.

      Interview questions [1]

      Question 1

      There were some algorithmic questions (basically they describe a case where you need to do a complicated search over large datasets, I solved the problem via KD-trees, which the reviewer seemed to like, but they ended up implementing a giant hash instead).
      Answer question
      10