Microsoft Machine Learning Engineer interview questions

53 Machine Learning Engineers have shared notes from their Microsoft interviews, with the questions they were asked in each round. That is 9% of the 597 Microsoft interview notes on Screna AI.

53 Machine Learning Engineer interview notes

Microsoft·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Microsoft ML Engineer interview with a classic algorithms question that had more depth to it than I expected. They wanted you to actually think through the trade-offs, not just spit out code.
  • Given an array of numbers, find the top K largest elements. Walk through multiple approaches and their trade-offs, then implement the heap-based solution.

“I started with sorting because it's the obvious answer and I wanted to show I could at least get there.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Microsoft MLE interview that was basically a stats and experimentation deep-dive with a short Python debugging exercise tacked on at the end. Pretty business-focused, not the leetcode grind I was expecting.
  • How would you design an A/B test for a product change, and what considerations would guide your setup?
  • How do you choose the appropriate statistical test for a given experiment?
  • What is a p-value and how would you explain it to a non-technical stakeholder making a product decision?
  • How do you handle the multiple comparisons problem when running several tests simultaneously?
  • Walk me through how you would estimate the required sample size for an experiment.
  • How do you think about the tradeoff between false positives and false negatives when making a product launch decision?
  • You're given a short Python snippet with bugs. Find them, fix them, and explain your reasoning.

“This was basically the anchor question the whole conversation orbited around.”

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

Jul 2026
System design round at Microsoft for an ML Engineer role. Just one question but it took the whole session and there's more to it than the name suggests.
  • Design a product search system.

“Spent probably too long on the query understanding layer and not enough on ranking.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft ML engineer coding round, basically a PyTorch implementation exercise where you fill in blanks to build a tiny next-token prediction model. Pretty hands-on, less theory and more 'can you actually write the code.'
  • Implement a small next-token prediction model in PyTorch using the architecture: Embedding -> Linear -> ReLU -> Linear. Fill in the missing pieces including the DataLoader setup, model layer definitions, and the forward method.
  • Given a batch of sequences, how do you prepare the inputs and targets for next-token prediction before passing them into the model?
  • After slicing inputs and targets from the sequence batch, how do you reshape them so all token positions are treated as a single flat batch before computing the loss?
  • Complete a training loop: run the forward pass, compute CrossEntropyLoss, backpropagate, and step the Adam optimizer.

“The architecture itself wasn't the hard part.”

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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·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·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft ML engineer interview, got asked a pretty standard regularization question. Nothing too wild but it's the kind of thing that trips you up if you haven't thought about it carefully in a while.
  • What are the differences between L1 and L2 regularization?

“I knew the basics, L1 pushes weights to zero so you get sparsity, L2 penalizes large weights but keeps them small rather than zeroing them out.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft ML Engineer interview with an array problem that looks simple but has a few edge cases worth thinking through carefully. Nothing too wild, just clean problem solving.
  • Given an unsorted integer array, find the minimum absolute difference between any two distinct elements, then return all pairs that achieve that minimum difference. Each pair should be ordered smallest to largest, and the list of pairs should be sorted ascending by first element.

“My first instinct was brute force, compare every pair, O(n^2).”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Microsoft ML engineer interview with a classic array problem that sounds trivial until you actually think about the constraints.
  • Given an array of integers ranging from 1 to n, determine whether the array contains any duplicate values using constant time and constant space.

“The constant space part is what tripped me up.”

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Microsoft·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Coding round for an ML Engineer role at Microsoft. Just one algorithmic problem but it was enough to make me think for a while.
  • Given an array of meeting time intervals, each with a start and end time, find the minimum number of conference rooms needed so no two overlapping meetings share the same room.

“Classic scheduling problem but I fumbled the first few minutes trying to brute force it before realizing a min-heap on end times was the cleaner path.”

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