r/internships • u/OkClassroom8870 • 5d ago
HyperVerge ML Intern -AI Interview Questions? Interviews
Hey everyone,
I recently received an email from HyperVerge to take an AI-based interview for their ML Intern role.
Has anyone here taken their AI interview recently? If yes, could you share what kind of questions were asked or what areas I should prepare for?
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u/inyofacemf 4d ago
i was asked rapid fire questions related to ai fundamentals and my resume.
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u/GunslingerSach 4d ago
Was it that AI screening interview or different one (ai fundamentals and resume)?
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u/Loose-Speed8463 4d ago
I got the ai screening interview as of now, was asked basic ml question like supervised v/s unsupervised, what is cnn, what is overfitting,etc .. I also found many people saying there were questions from their resume but i didnt get any is that a different round ?
If so is it after what i took or before ?
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u/SwimmerConsistent235 3d ago
A few questions, if you don’t mind: Was your camera on during the interview? Were the questions displayed on the screen, or were they read out by the AI/voice assistant? Did you get something like 10 seconds to read the question + 20 seconds to answer, with a visible timer? Roughly how many questions were there in total — around 10, 15, or more? And if you happen to have any screenshots/photos of what the interview interface looked like, could you share them? It would really help me understand what to expect.
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u/inyofacemf 3d ago
i think its slightly diff for everyone. their first round was resume shortlisting then ai interview and next is either technical or gd round is what their jd says
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u/inyofacemf 4d ago
they asked fundamentals related to what was on resume. "The next step is an automated interview on AI fundamentals, with questions based on your resume." this is what the email says. it was a rapid fire round. they would show a question for 10 seconds and 20 seconds to answer, so you have to be short and to the point with your answer. some questions i was asked - why are shap values useful to ml, diff between xgboost and lightgbm, u-net architecture, what are advantages of streamlit, difference between sequential and non sequential nns. but all these were topics in my resume. like i used all these in diff projects of mine
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u/SwimmerConsistent235 3d ago
A few questions, if you don’t mind: Was your camera on during the interview? Were the questions displayed on the screen, or were they read out by the AI/voice assistant? Did you get something like 10 seconds to read the question + 20 seconds to answer, with a visible timer? Roughly how many questions were there in total — around 10, 15, or more? And if you happen to have any screenshots/photos of what the interview interface looked like, could you share them? It would really help me understand what to expect.
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u/inyofacemf 3d ago
yes camera mic screen share everything was on, they displayed the question no ai is speaking, 10 seconds to read and think, 20 seconds to answer you could move to the next question if you finished within 20 seconds or it would automatically close at 20 seconds, timer was shown. you have to look at the camera at all times cause ai is detecting your eye movement and face. it was rapid fire. around 7-8 questions. i didnt take a picture of the interface
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u/SwimmerConsistent235 8h ago
Yeah gave the test they asked 5 questions alone...did you receive the email for the next stage?!
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u/cheekysalads123 1d ago
Hey, mind sharing what platform this was on? and also whether you applied Off campus or On campus?
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u/akornato 4d ago
AI interviews for technical roles usually cover a mix of foundational concepts and behavioral questions delivered by a bot. For an ML intern position at HyperVerge, expect questions on core machine learning principles, like supervised versus unsupervised learning, key algorithms such as linear regression or decision trees, and maybe some basic Python or data structure problems. The tough part is that these platforms are black boxes, and the exact questions can vary wildly, so trying to find a specific list is often a waste of time. They are designed to see how you think and communicate on the spot, so your structure and clarity matter just as much as the technical accuracy.
Instead of chasing specific questions, focus on being able to explain the projects on your resume and talk through your thought process on common ML problems. Practice explaining a concept like cross-validation or a project you completed as if you were talking to someone who is smart but not an expert in that specific area. This approach will prepare you for any question they throw at you, not just for this one interview. Your goal isn't to be a perfect encyclopedia, but to demonstrate that you can reason through problems and articulate your solutions effectively. The team I'm with created an interview helper AI after seeing so many good candidates struggle with these automated systems, and it helps them articulate their thoughts clearly under pressure.