Microsoft Machine Learning Engineer Interview Experience — LeetCode Rounds Then a RAG System Design

Round 1

The first round was a classic array-processing problem: LeetCode 56 (Merge Intervals).

Overall it wasn't too hard — mostly testing fundamentals. The core idea is to sort by interval start, then walk through and merge the overlapping ranges. What actually separates candidates is how "Pythonic" the code is and how you handle edge cases — things like sorting elegantly with a lambda, or handling empty input or a single-interval case.

For this round the interviewer cared more about whether your code was clean and readable, whether your logic was clear, and whether you explained your thinking out loud as you wrote rather than just silently coding.

Round 2

The second round adds a layer of testing your ability to switch mental models on top of a basic algorithm. The question was LeetCode 235 (Lowest Common Ancestor of a BST).

For a standard BST, you can find the answer quickly with a straightforward iterative or recursive approach that leverages the left-smaller/right-larger property.

Follow-up: what if the tree is changed to a regular binary tree with no ordering property?

At that point you have to switch your thinking from "exploit the structural property" to "general traversal" — i.e., the LeetCode 236 solution, using post-order traversal to search bottom-up. A lot of people get stuck here, because you need to quickly switch from the special-case solution to the general one — it's really testing your ability to transfer knowledge.

Round 3

This round was the core of the whole interview: the prompt was to design a "local sports news recommendation system," but what it was really testing was your understanding of getting LLM applications into production (RAG).

First you have to talk through the architecture choices — for example, whether to use Azure AI Search for vector retrieval or stand up your own Milvus; whether to go with fine-tuning or prompt engineering. Then it goes deeper into model deployment — how to reduce latency while keeping quality, which usually brings in optimization techniques like quantization and distillation.

Then it's guaranteed to get to a key question: hallucination. You need to explain how to reduce incorrect output — for example, adding retrieval augmentation (RAG) to ground answers in facts, adding a validation layer, or even designing something like a red-teaming mechanism to review outputs.