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Level: Intern
Round: Virtual Onsite · Type: Multiple Types · Difficulty: 4/10 · Duration: 240 min · Interviewer: Friendly
Topics: Machine Learning, K-means, Hypothesis Testing, Python Debugging, LLM, Behavioral Questions, Case Study
Location: Seattle, WA, US
Interview date: 2026-01-15
Question: Self-introduction, ML fundamentals including overfitting avoidance, recall/precision concepts, bagging vs. boosting differences, LLM basics including self-attention, prompt engineering, fine-tuning, PEFT, etc.
Question: Project explanation, implementation of k-means, and discussion of time complexity.
Question: Details are unclear, manager asked many questions related to the team's business, mostly related to hypothesis testing. I answered based on what I could recall. Included a simple Python debugging question.
Question: Project explanation and case study. Open discussion with positive feedback from the interviewer.
I had a phone screen, followed by three virtual onsite rounds.
Phone screen: I started with a self-introduction. Then, I was asked about machine learning fundamentals, including how to avoid overfitting, the concepts of recall and precision, and the differences between bagging and boosting. I also discussed LLM basics, including self-attention, prompt engineering, fine-tuning, and PEFT.
Virtual Onsite Round 1: I explained a project I had worked on and then implemented k-means. In the last few minutes, I was asked about the time complexity.
Virtual Onsite Round 2: I don't remember the specific details of this round. The manager asked many questions about the team's business, mostly related to hypothesis testing. I was not prepared for this and answered based on what I could recall. In the last ten minutes, I was asked to debug a simple Python problem.
Virtual Onsite Round 3: This round was with a PM. I explained a project and went through a case study. It was an open discussion, and the interviewer was very nice and actively asked questions and gave feedback.