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LinkedIn interview notes, page 10 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·Technical Phone Screen

May 2026
LinkedIn coding screen, graph traversal problem on a social network model. Pretty much a BFS question dressed up in their own domain language, which was a nice touch but didn't change what you had to do.
  • Given a Candidate interface with an id and a getConnections() method, implement a function that returns the shortest path length (in hops) between two Candidate nodes in a connection graph. Return -1 if no path exists.

“The social network framing is cute but once you see it's just shortest path on an unweighted graph, it's BFS and nothing else.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE coding round, one problem the whole session: LC 381, the follow-up to the classic insert/delete/getRandom question but with duplicates allowed. The index bookkeeping is where people fall apart and I was no exception.
  • Implement a RandomizedCollection data structure that supports insert, remove, and getRandom in average O(1) time, where duplicates are allowed and getRandom must return elements with probability proportional to how many times they appear.

“The array plus hashmap idea comes to you pretty fast, but the devil is in the swap.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn Data Engineer interview with a coding question focused on designing a MaxStack. The problem had more moving parts than it looked at first glance.
  • Design a MaxStack data structure that supports push, pop, top, peekMax (return max without removing), and popMax (remove the max element closest to the top if there are duplicates). All operations must keep the maximum tracking consistent.

“The basic push/pop/top part was fine, I've done stack problems before.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview, got a tree problem that I've seen before but still had to think through carefully. Pretty standard coding round overall.
  • Given the root of a binary tree and two nodes p and q, find their lowest common ancestor.

“I knew this one.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn system design round, one question the whole time. The problem sounds deceptively straightforward but there's a lot of surface area once you get into the scaling constraints.
  • Design a system that tracks the top K most frequently searched words and returns them in real-time or near real-time, at high query volume and update frequency.

“My first instinct was a simple hash map with a min-heap and I said so out loud, which I think was a mistake because it anchored the conversation on the naive solution for too long.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
SQL-heavy screen for a Data Scientist role at LinkedIn. Two questions, both off the same table, both requiring you to think carefully about self-joins and date filtering. Pretty focused session, no fluff.
  • Using the article_views table, write a query to count how many authors have never viewed any of their own articles.
  • On a specific date, how many members viewed more than one distinct article? Write the SQL to find this count.

“This one tripped me up for a second because the table uses both author_id and viewer_id and you have to compare them.”

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LinkedIn·Product Manager·Onsite - Behavioral / Leadership

May 2026
PM interview at LinkedIn, one behavioral question about off-roadmap work. Not much else to go on but the question itself was pretty meaty.
  • Tell me about a project you took on that wasn't originally planned. What triggered it, and how did you make the case that it was worth doing?

“This one tripped me up a little because my first instinct was to pick something that made me look proactive, but I realized mid-answer that the more interesting part is the 'how did you decide it was worth it' piece.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn technical screen focused on BST traversal. Pretty standard stuff but the dual requirement (k-th smallest AND largest) tripped me up a bit on the implementation side.
  • Given a binary search tree, find both the k-th smallest and k-th largest elements.

“I went straight for in-order traversal for the smallest, which is the obvious move.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn software engineer interview that was basically two big questions stretched across the whole session. The first half was a deep project walkthrough and the second half was an ambiguous production debugging scenario. Felt more like a working session than a traditional interview, which I wasn't totally prepared for.
  • Walk me through a complex project you led from start to finish, including the problem context, your responsibilities, stakeholders involved, architecture decisions, trade-offs you made across performance, cost, and delivery time, major risks and how you handled them, and what the measurable outcomes were. Then discuss alternative designs you considered and rejected, what you'd change looking back, and how you dealt with tough feedback or shifting priorities mid-project.
  • You're handed an ambiguous production issue you've never seen before. How do you approach it? Walk through how you'd break it down, what hypotheses you'd form, what telemetry or signals you'd look at, how you'd define success, which teams you'd pull in, how you'd structure a minimal proof of concept, and how you'd communicate progress or escalate if you got stuck.

“This one ate up probably 30 minutes.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview with a system design coding problem around a weighted LRU cache. Pretty meaty for a single session but the problem was well-scoped once I understood what 'weight' actually meant in this context.
  • Design and implement a cache where each entry has a weight, the cache has a fixed total weight capacity (not a fixed item count), and eviction follows LRU order. get and put should both run in O(1) average time.

“I spent the first few minutes confused about why weight mattered at all.”

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