Microsoft interview notes, page 37 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

Jun 2026

Microsoft SWE interview with a deep-dive into LLM decoding logic. The coding portion was more ML-adjacent than I expected, less leetcode and more 'do you actually understand how these models work under the hood.'

Implement greedy decoding for a language model. At each step you receive a logit vector over the vocabulary, pick the argmax token, append it to the sequence, and feed it back as input. Stop at an end-of-sequence token or after N tokens. Also discuss numerical stability, tie-breaking, and how this compares to sampling-based approaches.

“I knew greedy decoding conceptually but writing it out cleanly under pressure was a different story.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Did a technical screen for an ML Engineer role at Microsoft that focused on sequence decoding. Pretty implementation-heavy, no fluff.

Extend a greedy decoder to beam search with beam width k. Walk through how you'd maintain multiple partial hypotheses, score extensions using log-probabilities, and decide when to finalize.

“I knew beam search conceptually but fumbled the implementation details under pressure.”

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Microsoft·Technical Program Manager·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for a TPM role at Microsoft, just the one behavioral question from what I can tell. Pretty standard stuff but it still tripped me up a bit.

Describe a situation where you had to rely heavily on analytical thinking to solve a problem.

“I blanked for a second and reached for the first project that came to mind, which wasn't even my best example.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026

Microsoft SWE interview with a system design coding problem that looked deceptively straightforward. The core challenge was about weighted sampling across multiple data sources when the weights don't divide cleanly into a fixed batch size, which took me a minute to fully appreciate.

Design and implement a batch dataloader that draws samples from multiple data sources according to given weights. Each batch must be a fixed size, but the weights may not divide evenly into that size. How do you ensure the long-run proportions across batches stay close to the target weights?

“The easy case (weights like 0.3/0.3/0.4 with batch size 10) clicks immediately, three items, three items, four items, done.”

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Microsoft·Technical Program Manager·Onsite - System Design / Architecture

Jun 2026

Interviewed for a TPM role at Microsoft, got a question around data architecture. Not much else to go on from my notes but it was a technical conversation.

How would you approach designing a data architecture for a large-scale system?

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Microsoft ML engineer interview with a pretty gnarly coding problem around stop-sequence detection for LLM inference. One question, but it went deep fast and touched on edge cases I hadn't fully thought through before.

Implement stop-token detection for an LLM inference loop. You're given a streaming token generator and a list of stop sequences (each can be multiple tokens long). Terminate generation the moment any stop sequence is fully matched and return only the text before the match. Be prepared to discuss correctness when a stop sequence spans multiple tokens and how to handle overlapping or prefix-conflicting stop sequences.

“I started with the naive approach, buffer the last N tokens and check for a match after each new token, where N is the length of the longest stop sequence.”

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Microsoft·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Interviewed for a Customer Success Manager role at Microsoft, just the one behavioral question from what I can tell. Pretty standard stuff but still worth thinking through before you go in.

Can you describe a situation where you had to adapt to a last-minute change?

“I had a decent story ready but fumbled the delivery a bit.”

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Microsoft·AI Engineer·Onsite - System Design / Architecture

Jun 2026

Microsoft AI Engineer system design round, one big question about deploying an ML model with strict latency guarantees. Took up the whole session and went pretty deep into serving infrastructure, optimization tradeoffs, and how you actually enforce an SLA when things go sideways.

Design a production deployment system for a trained ML model that must meet strict response-time guarantees. Walk through your serving stack, how you'd break down the latency budget, optimizations you'd apply, and how you'd handle tail latency and SLA breaches.

“This question sprawled in every direction and I don't think I managed it well at first.”

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Microsoft·Data Scientist·Technical Phone Screen

Jun 2026

Microsoft data science technical phone screen that went pretty deep into algorithms territory. The problem looked like a search question on the surface but kept expanding into edge cases and proofs, which I was not fully ready for.

You have a sorted non-decreasing integer array of unknown length. You can only access it through an API that returns +infinity for out-of-bounds indices. Implement a lower_bound function that returns the smallest index where the value is >= target, or -1 if no such index exists. Your solution must run in O(log n) time.
How does your approach handle edge cases: an empty array, all values less than the target, all values greater than or equal to the target, and duplicate values?
Prove the correctness of your exponential search plus binary search solution, and give a tight upper bound on the total number of API calls made.
How would your approach change if the array might have been rotated once, first without duplicates and then with duplicates?

“I knew exponential search was the move but fumbled explaining why doubling the index works for bounding n.”

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Microsoft·Software Engineer·Technical Phone Screen

Jun 2026

Microsoft SWE coding round, one question the whole time. Sliding window problem that maps pretty cleanly to a known LC problem, so if you've done your prep it's manageable.

You're given a binary array where 1 means land and 0 means ocean, plus an integer representing how many 0s you can flip to 1. Find the maximum length of contiguous land you can create using at most that many flips.

“Recognized this pretty fast as a sliding window setup.”

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