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Microsoft interview notes, page 45 of 60

FAANG / Big Tech

Most of these Microsoft write-ups come from Software Engineers (372), then Product Managers (124) and Machine Learning Engineers (53). The most reported rounds are the technical phone screen (255), the product sense round (111), the system design round (93) and the behavioral / leadership round (49).

597 interview notes · updated Oct 2026

Microsoft·Software Engineer·Onsite - Multi Round

Jun 2026
Microsoft SWE round that mixed behavioral questions with a surprisingly meaty coding/design block. The design portion went deep on rate limiting algorithms and distributed systems, which I didn't fully expect from what was billed as a coding round.
  • Tell me about a time you had to make a significant technical trade-off under constraints. How did you decide, and what was the outcome?
  • Describe a situation where you had to collaborate with someone who had a different technical opinion. How did you handle the disagreement?
  • Design and implement a rate limiter for a per-user API. Walk through your algorithm choices and their trade-offs.
  • Implement a thread-safe rate limiter. What data structures and synchronization mechanisms would you use?
  • How would you extend your rate limiter to work in a distributed environment across multiple servers?

“Standard behavioral stuff.”

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Microsoft·Software Engineer·Hiring Manager Screen

Jun 2026
Went through a software engineer interview at Microsoft that covered the usual bases: background, motivation, and a deep dive into past project work. Pretty standard structure but the project question had more follow-up than I expected.
  • Give a brief introduction of yourself focusing on the roles you've held, the scope of your work, and what you actually accomplished.
  • Why Microsoft specifically? What draws you to this company over others?
  • Walk me through the project you're most proud of. What were your goals, what technical or leadership challenges did you face, what trade-offs did you make, and what were the measurable results?
  • If we made you an offer, when could you start and are there any relocation or timing constraints we should know about?

“Kept it tight, maybe too tight.”

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Microsoft·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
Microsoft ML Engineer interview that went deep on data pipeline design, specifically around cleaning and preparing a messy OCR dataset for LLM training. One long technical question that branched into four or five sub-topics, so it felt more like a working session than a traditional interview.
  • You're given a raw OCR-extracted text dataset intended for LLM training or fine-tuning. Walk through your full pipeline: how do you diagnose OCR noise, filter and deduplicate, normalize the data, and ultimately validate dataset quality before and after cleaning?

“This question is basically four questions glued together and I did not pace myself well.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Microsoft behavioral round, one question but they really wanted to dig into it. Less about what happened and more about how you think through conflict and trade-offs in real time.
  • Tell me about a time you disagreed with someone on a technical design or implementation decision. What was the disagreement, how did you resolve it, and how did the project still get delivered successfully?

“The part that tripped me up was the follow-up.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026
Microsoft SWE coding round, one problem the whole time. It was a binary search question dressed up in a word problem, which I didn't immediately clock, so I wasted a few minutes thinking about prefix sums before the right approach clicked.
  • You have a sorted integer array and a starting index. Find the maximum number of consecutive elements starting from that index whose sum is strictly less than a given target. Design an efficient algorithm and analyze its complexity.

“I went straight to prefix sums and a linear scan, which works but the interviewer pushed me on whether I could do better.”

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Microsoft·Data Scientist·Technical Phone Screen

Jun 2026
Three pretty dense coding problems for a Data Scientist role at Microsoft. The questions leaned heavily algorithmic, which I wasn't fully expecting. Left feeling okay about two of them and genuinely shaky on the third.
  • Given a sorted integer array and a value k, modify the array in-place so each distinct element appears at most k times. Return the new valid length. Must run in O(n) time with O(1) extra space.
  • Find the shortest substring of s that contains all characters of t (with correct frequencies). If there's a tie, return the leftmost. Must run in O(|s| + |t|) time and handle Unicode code points correctly.
  • Implement a sanitize function that trims whitespace, collapses internal whitespace runs into a single space, and replaces every maximal run of ASCII digits with a single '#' token. Do it in a single O(n) pass with O(1) extra space. Discuss multi-byte Unicode and surrogate pair pitfalls.

“Two-pointer setup is the right instinct but I kept second-guessing my write index.”

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Microsoft·AI Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Microsoft for an AI Engineer role, focused entirely on distributed RL training infrastructure. One long question, lots of follow-ups, and I left feeling like I'd only covered about 60% of what they actually wanted to hear.
  • Design a PPO training system where rollout collection, policy evaluation, and gradient updates are fully decoupled so they can scale independently without blocking each other. Walk through the architecture, how weights stay in sync, and how you handle the fact that actors may be running a stale policy.

“This is a meaty one.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft MLE interview that went pretty deep into ML fundamentals and NLP. Covered a lot of ground from evaluation metrics to regularization to modern LLM stuff. More conceptual than I expected but not in a bad way.
  • What are precision and recall, when does each one matter more, and how do you think about the trade-off between them?
  • Explain the ROC curve and AUC. When can AUC be a misleading metric?
  • Walk through L1 vs L2 regularization. What's the geometric intuition, and how do you decide which one to use?
  • Beyond accuracy, what classification metrics do you reach for and when? Things like F1, log-loss, calibration.
  • What do you know about using reinforcement learning to fine-tune large language models, and how do agentic or tool-using LLM systems work?

“Felt pretty solid here.”

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Microsoft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Microsoft PM interview with a classic moonshot product design question. Short on details about how it went, but the question itself is the kind that separates people who have a framework from people who actually have opinions.
  • What would you build and why? Walk through a full end-to-end moonshot-style product example.

“This is the kind of question where you can tell who prepped and who actually thinks about products.”

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Microsoft·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed for a PM role at Microsoft and got hit with the classic strengths and weaknesses question. Nothing too wild, but it's the kind of question that's easy to fumble if you haven't thought it through.
  • What are your strengths and weaknesses as a product manager?

“I had an answer ready but it felt a little rehearsed coming out.”

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