LinkedIn interview notes, page 32 of 32

Large Enterprises

Most of these LinkedIn write-ups come from Software Engineers (156), then Product Managers (66) and Data Scientists (37). The most reported rounds are the technical phone screen (169), the product sense round (62), the system design round (42) and the behavioral / leadership round (20).

316 interview notes · updated Oct 2026

LinkedIn·Software Engineer·Onsite - System Design / Architecture

Jul 2026

LinkedIn system design round where I had to walk through building a company-wide monitoring platform from scratch. Pretty open-ended, which sounds nice until you realize you have to structure it yourself. Covered a lot of ground across metrics, logs, and traces.

Design a company-wide monitoring system that supports metrics, logs, and distributed traces across many services and teams.
How would you handle high ingestion volume and prevent the pipeline from falling over under load?
What storage choices would you make for time series metrics versus logs, and how do they scale differently?
How do you design the alerting pipeline to stay reliable even when parts of the monitoring system itself are degraded?
How would you handle cardinality explosions in your metrics system, and what controls would you put in place?

“This is the kind of question where you can go in ten directions and none of them feel wrong, which is its own problem.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jul 2026

LinkedIn SWE interview with a nested list problem that looks straightforward until you realize the weights run backwards relative to depth. One question, but it had enough hidden complexity to keep me busy for a while.

Given a nested list of integers where each integer has a depth based on how many lists it's inside, compute the inverse-depth weighted sum. The weight of an integer at depth d is (max_depth - d + 1), so deeper integers get lower weight. Return the total weighted sum.

“My first instinct was to just do a DFS and track depth, which is fine, but I didn't immediately clock that you need the max depth before you can assign any weights.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jul 2026

LinkedIn software engineering interview with a coding question focused on string manipulation. Pretty standard technical screen but the edge case requirements made it more involved than I expected.

Write a function to check if a string is a palindrome after stripping out non-alphanumeric characters and ignoring case. Include time and space complexity analysis, and write tests that cover edge cases like special characters and Unicode input.

“I got the basic version down pretty fast but the Unicode part threw me off more than I'd like to admit.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jul 2026

LinkedIn SWE interview with a linked list problem that sounds easy until you actually have to hit O(1) space. The question had a few wrinkles they pushed on pretty hard.

Given two singly linked lists that may share a common tail, find the first node where they intersect. Nodes are compared by reference. Aim for O(m+n) time and O(1) space. Walk through your approach, correctness reasoning, complexity analysis, and edge cases like empty lists, no intersection, or very different lengths.

“The length-difference trick clicked for me pretty fast: get both lengths, advance the pointer on the longer list by the difference, then walk both in sync until you hit the same node reference.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jul 2026

LinkedIn data scientist interview with a meaty A/B testing case built around Simpson's paradox. One question, but it had a lot of layers and the follow-ups kept coming.

A marketing team ran an A/B test on two email versions across two cities and two weeks. The pooled data shows version A winning, but version B wins in every city-level and week-level breakdown. How do you figure out which email is actually better? Walk through your choice of primary and guardrail metrics, how Simpson's paradox shows up here, what confounders you'd investigate, how you'd reanalyze the data, whether confidence intervals are still valid, and how you'd redesign the experiment if the original allocation was imbalanced.

“This one sprawled in a way I wasn't ready for.”

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LinkedIn·Software Engineer·Technical Phone Screen

Sep 2026

LinkedIn SWE coding round where I got a tree problem, implemented a working iterative postorder traversal, and then spent the bulk of the interview in a back-and-forth about space complexity and a line-by-line dry run. Only finished one question total, which has me second-guessing how the round will be read.

Given a tree, return the nodes in the order they would be removed if you repeatedly removed all leaf nodes, grouping nodes by their height from the leaves.

“I went with an iterative postorder traversal using a stack where each frame tracked the node, a child iterator, and the max height seen so far among processed children.”

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