Let’s share this time Microsoft The complete interview process of AS 26 Intern, from phone screen to 3 rounds of VO, the pace of the whole process is not particularly high-pressure, but the difference in style is quite obvious. The overall feeling is: the difficulty is medium, and the direction depends on the background of the group and interviewer.
Phone Screen
Phone Screen starts with a self-introduction and then quickly moves into ML basic Q&A.
The interviewer mainly focused on the basic concepts of machine learning, such as how to avoid overfitting, understanding of Recall and Precision, and the difference between Bagging and Boosting. This part is obviously to see if the foundation is solid. It will not specifically ask about the derivation of formulas, but it will focus on whether you understand the logic behind it, rather than just memorizing definitions.
The second half turns to LLM-related issues. I asked about the core idea of the Self-Attention mechanism, and also talked about prompt engineering, fine-tuning, and PEFT, an efficient parameter fine-tuning method. The overall feeling is that as long as the concepts are clear and you can explain why these methods are needed and what scenarios each is suitable for, you can basically pass the test. The rhythm is relatively steady, and there is no particularly tricky digging.
VO Round 1
The first round is a combination of Behavioral and technology.
Let me start by talking about a project I have done. The interviewer will follow the details of the project and ask questions, such as why you chose this model at the time, whether you compared other solutions, how to design the experiment, how to verify the results, and whether you encountered any failed versions. The whole process is more like breaking down whether you actually did it than listening to your story.
The second half is live handwritten K-means. It is required to write a complete process, including initializing center points, allocating samples, updating centers, and judging convergence conditions. After the code was written, the interviewer asked about time complexity and how to optimize it if the amount of data is very large, such as whether mini-batch can be done and whether it can be processed in parallel.
The overall pace of this round was quite comfortable, there was no time jam, and a few minutes were left for me to ask questions at the end.
VO Round 2
The second round was the manager interview, and the style changed significantly.
The questions in this round are more centered around the business scenarios within the group, with a large focus on hypothesis testing and experimental design. For example, if the effect of a feature does not improve significantly after it goes online, how to judge whether it is really effective; how to explain if the performance of multiple indicators is inconsistent; how to design the A/B test and how to ensure statistical significance.
To be honest, I was not fully prepared for this part. I answered many questions based on my own understanding, focusing on sample size, significance test, and confounding factor. The whole thing is not particularly technical, but more focused on thinking ability and business understanding.
In the last ten minutes, I was given a Python debug question, which was not very difficult and mainly focused on finding logic bugs. This part stabilizes the rhythm and prevents the whole round from being an abstract discussion.
VO Round 3
The last round was the PM session, and the overall atmosphere was very relaxed.
At the beginning, we still talked about the project, but we focused more on product value and implementation effects rather than model details. Then I did a case study, which was more like an open discussion. For example, if you want to build a certain function, how would you define success indicators? What should you do if the user experience declines after the model is launched? How do you trade-off between accuracy and latency.
The interviewer was quite interactive throughout the process, and would constantly ask questions based on my answers, and would also give me some feedback. It's more like discussing a real product than an exam.
The overall experience is good and not stressful.
Overall feeling
The difficulty of this line is not particularly high, but the difference in direction is obvious. The first round focuses on ML basics and algorithmic capabilities, the second round focuses on experimental design and business understanding, and the third round focuses on product thinking. It is recommended to prepare for LLM related content, but do not bet excessively. This time I cooperated with programhelp’s interview assistant, and my status changed to offer after a week of interview. If you are preparing for a similar position and your foundation is not stable enough, or you have no confidence in VO, you can actually find programhelp for targeted assistance. VO assist . Simulate high-frequency questions in advance, and have real-time reminders of ideas at critical moments. Stable output will really make it much easier.
