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Microsoft interview notes, page 51 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 - System Design / Architecture

Jun 2026
Microsoft system design round, focused entirely on a ticket booking system. Pretty deep dive, way more edge cases than I expected going in.
  • Design a ticket booking system for something like a movie theater, concert, or train. Walk through the core user flow from browsing events to confirming a booking.
  • How do you prevent two users from booking the exact same seat simultaneously?
  • How would you handle a surge of users trying to book seats for a very popular event all at once?
  • How would you integrate a payment provider, and how do you ensure idempotency if a payment request is retried?
  • How do you handle refunds, cancellations, and partial cancellations when a group books together?
  • How do you scale the read-heavy parts of the system, like event search and seat map fetching, without compromising consistency on writes like seat reservation?

“I started with the happy path which felt right, browse events, pick seats, pay, done.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026
Microsoft SWE interview with a matrix manipulation problem that had more edge cases than I expected. The in-place constraint is what made it actually hard.
  • Given a large matrix and a sub-matrix defined by its corner coordinates, move the sub-matrix to a new position within the larger matrix. You must do this in-place without allocating a second matrix of the same size. How do you handle overlapping source and destination regions, and how do you order your reads and writes to avoid corrupting data?

“I jumped straight to the non-overlapping case and coded it up fine, then the interviewer asked what happens when source and destination overlap.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026
Microsoft SWE interview with a grid rotation problem that sounds trivial until you're actually talking through it live. The follow-up on generalization and in-place vs out-of-place tripped me up more than I expected.
  • Given a 3x3 grid of numbers, return the grid after a 180-degree rotation. Then discuss in-place versus out-of-place implementations, and how your solution generalizes to an n by m grid.

“I got the basic rotation right pretty fast, reversing the flattened list and reshaping.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Virtual onsite at Microsoft for an Applied Scientist role, with back-to-back sessions covering a research deep-dive and standard behavioral questions. Nothing too surprising but you really need to have your research story tight and your examples ready to go.
  • Walk us through your research. What problem were you solving and why does it matter?
  • Tell me about a time you had to work through significant ambiguity on a project. How did you handle it?
  • Describe a situation where you had to take ownership of something that wasn't strictly your responsibility.
  • What motivates you to work on the kinds of problems you've been working on?
  • Give an example of a time you collaborated with people outside your immediate team to get something done.

“This was basically the whole first chunk of the interview.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026
Microsoft SWE interview with a classic bit manipulation problem. Nothing too wild, but the Gray code question had a slick trick behind it that I almost missed entirely.
  • Given an integer n, return any valid n-bit Gray code sequence, where every adjacent pair of integers in the sequence differs by exactly one bit in binary, including the first and last elements.

“I knew what a Gray code was vaguely but couldn't remember the formula under pressure.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft ML Engineer interview, technical phone screen focused entirely on ML fundamentals and experiment design. Three questions, no fluff, they went pretty deep on the third one.
  • For a binary classification problem, walk through precision, recall, F1, the confusion matrix terms, ROC/AUC, and when you'd prefer a precision-recall curve over ROC.
  • Explain L1 vs L2 regularization: the math, the effect on weights, and how you'd choose between them in practice.
  • You have an NLP pipeline with several components like preprocessing, an encoder, a retrieval module, a prompt template, a reranker, and decoding settings. How would you design an ablation study to figure out which components actually matter?

“Felt solid on the basics but stumbled a bit when they pushed on why PR curves matter under class imbalance.”

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Microsoft·Technical Program Manager·Onsite - Product Sense / Strategy

Jun 2026
TPM interview at Microsoft focused on product strategy for Surface hardware, specifically around AI-powered features. Single question but it had a lot of surface area (no pun intended) and I wasn't fully prepped for the hardware-software intersection angle.
  • How would you plan and execute the launch of a Copilot Plus feature on Surface devices?

“I spent the first minute thinking about this purely as a software rollout and the interviewer kind of let me run with it before nudging me toward the hardware constraints.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Applied Scientist internship screen at Microsoft covering ML fundamentals. Three topic areas back to back: bias-variance, classification metrics, and confidence intervals. Felt like a written exam more than a conversation, but the questions were fair if you actually know your stuff.
  • Define bias and variance in supervised learning, explain the tradeoff between them in the context of underfitting and overfitting, and give a few practical ways to reduce each.
  • Define accuracy, precision, recall, and F1. When does accuracy give a misleading picture? Given a confusion matrix with TP, FP, TN, and FN values, how would you compute each metric and decide which one to optimize for an imbalanced dataset?
  • What is a confidence interval? If you evaluated a binary classifier on a test set of size n and got accuracy p-hat, how would you compute a 95% CI for the true accuracy, and what assumptions does that require? What alternative methods exist when those assumptions don't hold?

“This felt like a warmup but I still fumbled the practical part a little.”

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Microsoft·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Microsoft for a financial security product role. One question, pretty meaty, focused on AI applications in banking security. No fluff, they just dropped it on me and waited.
  • You're a PM for a financial security product. How would you apply AI to improve security across banking systems?

“I went broad first which was probably a mistake.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026
Microsoft SWE coding round, one question the whole time. Pretty focused session, no small talk, just straight into the problem.
  • Given an integer array with positive and negative numbers, find the sum of the longest contiguous subarray made up entirely of positive numbers. If there's a tie in length, return the highest sum. Return 0 if no positive numbers exist. Walk through the algorithm, its complexity, and write working code.

“Took me a minute to get the edge cases straight in my head.”

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