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Microsoft interview notes, page 14 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·Technical Phone Screen

Apr 2026
Microsoft SWE interview, one technical round focused on a DNA sequence chaining problem. Pretty algorithmic, more depth than I expected for what seemed like a straightforward ordering task.
  • You're given a list of records, each with a start, end, and payload field. The records form a chain where one element's end matches the next element's start. Reorder them into a valid chain and return the concatenated payloads in order.

“My first instinct was to just sort, which would've been wrong.”

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Microsoft·Technical Program Manager·Onsite - Behavioral / Leadership

Apr 2026
Prep material for a Senior TPM behavioral loop at Microsoft Azure, focused on cross-team launch leadership. The content is structured around a single compound prompt covering alignment, conflict, and customer feedback recovery. No actual interview outcome is recorded.
  • Tell me about a time you led a product or platform launch that involved multiple engineering teams. How did you get everyone aligned?
  • Describe a conflict you had with an engineer or engineering team during a launch. How did you resolve it?
  • What did you do when the solution you launched did not fully meet customer expectations?

“This is the kind of question where you can burn two minutes on context and never get to the part they actually care about.”

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Microsoft·Software Engineer·Onsite - Multi Round

Apr 2026
Microsoft SWE interview focused entirely on a deep project walkthrough. One question, but it sprawled into like five different conversations depending on how you answered. Not a casual chat.
  • Walk me through one of your most significant projects in depth, including the design decisions you made, how you handled scope and stakeholder dynamics, what broke, how you recovered, the measurable impact, and what you'd change looking back.

“This is the kind of question where you think you're ready and then the follow-ups expose every corner you didn't prepare.”

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Microsoft·Machine Learning Engineer·Recruiter / HR Screen

Apr 2026
Screener for an ML engineer role at Microsoft. Just one question about Qualcomm, which felt oddly placed for a Microsoft interview.
  • What do you know about Qualcomm?

“Weird question to get in a Microsoft interview.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Microsoft software engineering interview focused almost entirely on behavioral rounds, with three main prompts covering ambiguity, technical disagreement, and a self-intro deep dive. No coding or system design on the slate, which surprised me a little.
  • Tell me about a time you had to deliver something with very little information or guidance. How did you decide what to prioritize?
  • During that same delivery, did you introduce a code freeze? Walk me through that decision.
  • How did you maintain parity between systems or versions during a migration?
  • Describe how you ran an A/B experiment or staged rollout, including how you controlled risk, defined success metrics, and handled rollback.
  • Describe a significant disagreement you had with another engineer or stakeholder over a technical design. What was the disagreement, how did you work through it, and what shipped in the end?
  • Give a brief intro of your background and walk through one or two projects, then be ready to answer detailed questions about the decisions you made.

“This one has a lot of surface area.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral screening round for a Firmware Engineer role at Microsoft. No coding, no technical fundamentals, just back-to-back behavioral questions and project deep-dives. More thorough than I expected for a screening.
  • Walk me through a past project in detail, including the architecture, your specific contributions, the trade-offs you made, and the outcome.
  • Tell me about a time you had to collaborate closely with others to get something done. How did you handle disagreements or friction within the team?
  • Describe a situation where you took ownership of something that wasn't strictly your responsibility.
  • Tell me about a time something failed or didn't go as planned. What did you learn from it?

“This is where I stumbled a bit.”

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

Apr 2026
Microsoft interview for what seemed like an ML or applied engineering role. Single design question, pretty open-ended, left me unsure if I went deep enough or just rambled.
  • Design an image classifier from scratch.

“I started with data pipeline stuff and the interviewer kind of just waited.”

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Microsoft·Software Engineer·Technical Phone Screen

Apr 2026
Microsoft SWE coding round, one classic sliding window problem. Nothing fancy, but the kind of question that humbles you if you haven't touched it in a while.
  • Find the longest substring that contains no repeating characters.

“Knew this problem but still fumbled the implementation a bit.”

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

Apr 2026
Microsoft ML Engineer interview that went deep on infrastructure design. One meaty system design question that took up most of the session, covering a lot of ground around checkpointing, reproducibility, and parallel training correctness.
  • Design a resumable DataLoader for ML training that supports mini-batch iteration over a shuffled dataset, checkpoint/restore of iteration state (including permutation, index, and RNG state), and deterministic replay after resuming. Also discuss correctness in multi-worker and multi-epoch scenarios.

“This one is trickier than it looks because saving 'current index' is not enough.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Microsoft ML engineer interview with a focus on evaluation metrics for a retrieval API. The question was deceptively practical and the edge case follow-up is where things got interesting.
  • You have 10 image files with ground-truth labels for whether each contains a dog. Given an API that returns k file IDs predicted to be dogs, write pseudocode to compute precision and recall against the ground truth.
  • How would you make the metric computation robust when the API behaves unexpectedly, such as returning None, throwing an exception, returning fewer or more than k items, returning duplicates, or returning unknown file IDs?

“Felt pretty solid on this part.”

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