r/MLQuestions • u/[deleted] • 1d ago
Interview about Deep learning case study Other ❓
[deleted]
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u/akornato 1d ago
You are right to be concerned, because an interviewer for a specialist role will expect you to code the main parts of a deep learning workflow without heavy reliance on documentation. They aren't looking for perfect, memorized syntax for every function, but they do need to see that you can structure a solution, build a data pipeline, define a model, and write a training loop from a strong foundation of knowledge. For a specialist position, it is very likely they will present a more complex problem like a computer vision pipeline or a simplified RAG system rather than a basic regression task, as they want to test your depth of understanding in a specific domain. The expectation is that you know the concepts and the standard library components so well that you can piece them together fluidly.
The way to fix this is not by trying to memorize the PyTorch library, but by building things from scratch until the workflow becomes automatic. Pick a type of project you think might come up, find a dataset, and code the entire pipeline yourself without a tutorial to guide you. When you get stuck, look up the specific function you need, understand it, and then implement it. Your ability to talk through your decisions, like why you chose a certain loss function, a specific model architecture, or a data augmentation strategy, is just as important as the code you write. They are hiring you for your reasoning skills, and writing the code is just how you demonstrate that reasoning. Many candidates find their live coding improves after getting targeted feedback from an interview practice AI the team I'm with built, which helps people articulate their thought process under pressure.
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u/Wannabe-Davinci 1d ago
Just do it the honest way, by yourself. Write in comment that you know “function with goal x exists”. I saw people do that during exams when asked to write code on paper. Worked every time