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Microsoft interview notes, page 18 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 - Behavioral / Leadership

May 2026
Interviewed for an EM role at Microsoft, behavioral focus, one question that sounds easy until you're actually in it.
  • How do you handle situations where your team's goals are not aligned with the broader company direction?

“I went straight into a story about advocating for my team and kind of made myself sound like I was fighting the company.”

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Microsoft·Software Engineer·Technical Phone Screen

May 2026
Microsoft SWE interview with a scheduling/intervals problem that's pretty much a classic at this point. Nothing shocking but it's the kind of question that punishes you if you haven't drilled the heap-based approach.
  • Given an array of meeting time intervals, what is the minimum number of conference rooms needed so that no two overlapping meetings are scheduled in the same room?

“I knew the problem but fumbled the implementation a bit.”

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Microsoft·Software Engineer·Technical Phone Screen

May 2026
Microsoft SWE interview with a calendar booking design problem. Pretty focused session, just the one question but they pushed on edge cases and complexity tradeoffs.
  • Design a calendar booking system that stores half-open time intervals. Implement a `book(start, end)` method that returns true and adds the event if it doesn't conflict with any existing booking, and false otherwise.

“The overlap condition tripped me up for a second.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

May 2026
Round 2 at Microsoft for an MLE role. It was a project deep dive combined with LLM and Transformer questions, which is a pretty dense combo for a single session.
  • Walk me through a past project in depth, covering your technical decisions and what you would change.
  • How do large language models work, and what are the key components that make them effective?
  • Explain the Transformer architecture and how attention mechanisms function within it.

“The project deep dive part is where I always feel like I'm either over-explaining or underselling.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

May 2026
Microsoft ML engineer interview with a meaty coding question on text generation decoding. One question, but it sprawled into a full design and implementation discussion that covered a lot of ground.
  • Implement beam search decoding and nucleus (top-p) sampling for an autoregressive language model, given a function that returns logits at each step. Discuss complexity, numerical stability, and the trade-offs between different decoding strategies.

“This was basically two coding problems stitched together with a design discussion at the end.”

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Microsoft·Data Scientist·Technical Phone Screen

May 2026
Technical screen for a Data Scientist role at Microsoft. One question, but it was a beast: a four-part deep dive on CNNs, RNNs, and LSTMs that covered theory, math, parameter counting, and experimental design all in one shot. Left feeling like I'd taken an exam.
  • Compare CNNs, RNNs, and LSTMs for sequence modeling across four dimensions: (1) inductive biases and when you'd prefer a dilated 1D CNN vs. RNN/LSTM, and when LSTM beats vanilla RNN; (2) the math behind vanishing/exploding gradients in vanilla RNNs and how LSTM gates address it; (3) parameter counts and parallelism tradeoffs for a 1D CNN, GRU, and LSTM given a specific input shape; (4) how you'd design an ablation study to pick among these models under tight latency and limited labeled data constraints.

“This was basically four questions stapled together and I did not clock that until I was already halfway through part one.”

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Microsoft·Software Engineer·Technical Phone Screen

May 2026
Microsoft Applied Scientist interview with a classic ML theory question on normalization. Pretty standard technical screen, one question but they really wanted you to go deep on the details rather than just name-drop the terms.
  • What is the difference between Layer Normalization and Batch Normalization? Walk through what dimensions each normalizes over, how they behave differently at train vs inference time, their dependence on batch size, and when you'd choose one over the other.

“I knew this one but still fumbled the train vs inference part for BatchNorm.”

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Microsoft·Software Engineer·Technical Phone Screen

May 2026
Microsoft SWE interview with two coding questions back to back. One tree problem, one DP string problem. Nothing too wild but the combo felt like a lot to get through cleanly in one session.
  • Given a binary tree, return its node values level by level from bottom to top, where each level lists nodes left to right but the levels themselves are ordered deepest first.
  • Given a string, find the length of its longest palindromic subsequence.

“BFS then reverse the result.”

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Microsoft·Software Engineer·Hiring Manager Screen

May 2026
Interviewed for a Customer Success Manager role at Microsoft. Just one question worth noting, pretty philosophical, not what I expected going in.
  • What is your philosophy on technology?

“Broad question and I kind of rambled.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Microsoft SWE interview, mostly behavioral with a self-intro to kick things off. The whole thing ran about 45 minutes and the BQ portion ate up most of that time, with questions angled toward real-world AI use cases rather than generic leadership stuff.
  • Tell me about yourself and your background.
  • Describe a situation where you applied AI or machine learning to solve a practical problem.

“Standard opener, nothing to overthink.”

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