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LinkedIn interview notes, page 5 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·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
LinkedIn PM interview, one question about how you'd measure and compare a mousetrap product against competitors. Pretty open-ended and I wasn't totally sure where they wanted me to go with it.
  • How would you measure the success of a mousetrap, and what would tell you that your version is better than what's already on the market?

“I started with user outcomes, like did the mouse actually get caught, and then tried to layer in things like time-to-catch, safety, ease of disposal.”

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LinkedIn·Product Designer·Onsite - Product Sense / Strategy

Apr 2026
LinkedIn product design interview, one question about building a full onboarding flow from scratch. Pretty open-ended and I wasn't totally sure how deep they wanted me to go.
  • Design the end-to-end user onboarding experience for a mobile or web app.

“I jumped straight into screens and flows without anchoring on what kind of app or what success looks like for a new user.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Apr 2026
LinkedIn ML engineer interview with a math-meets-coding problem that felt more like a puzzle than a typical coding screen. Not what I expected going in.
  • Given a sorted array, apply the quadratic formula to each element and return the results in sorted order.

“My first instinct was to just map the formula and re-sort at the end, which works but they clearly wanted something smarter.”

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LinkedIn·Data Scientist·Technical Phone Screen

Apr 2026
LinkedIn Data Scientist interview with a SQL-heavy analytical task. The problem was structured in two parts and centered on a job activity table, escalating from basic aggregation to a tricky filtering edge case that I didn't fully see coming.
  • Given a job activity table with job_id, candidate_id, and activity_type (either 'view' or 'apply'), calculate for each job: total activities, unique candidates who viewed, and unique candidates who applied.
  • Recalculate those same metrics but exclude any candidate who applied without ever having viewed any job. How do you handle someone who applies to multiple jobs but has zero views across all of them?

“Pretty standard aggregation stuff.”

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LinkedIn·Software Engineer·Technical Phone Screen

Apr 2026
LinkedIn SWE interview that went pretty deep into memory management fundamentals. Not what I expected from a systems round but they clearly wanted to see if you actually understood what's happening under the hood, not just surface-level definitions.
  • Compare heap and stack memory in detail: how they're allocated and freed, data lifetimes, access patterns, performance characteristics, thread visibility, common pitfalls, and when you'd choose one over the other. Also discuss how recursion depth and large objects factor into the decision.

“This question is way broader than it looks.”

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LinkedIn·Software Engineer·Technical Phone Screen

Apr 2026
LinkedIn coding screen, one algorithm question the whole time. Pretty standard binary search territory but the no-sqrt constraint trips you up if you haven't seen it before.
  • Given a non-negative integer n, determine whether it is a perfect square without using any built-in square root function. Your solution should run in O(log n) time.

“My first instinct was to just iterate from 1 upward and check k*k == n, which works but is obviously O(sqrt n).”

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LinkedIn·Software Engineer·Technical Phone Screen

Apr 2026
LinkedIn coding screen, one question about parsing a service dependency map and finding which services touch any deleted paths. Pretty straightforward once you see it, but the setup in JSON threw me off at first.
  • You're given a JSON describing services and their read/write path dependencies. Given a list of deleted paths, return all services that read from or write to any of those paths.

“My first instinct was to overcomplicate it.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

Apr 2026
LinkedIn system design round for a software engineering role. The whole session was focused on building an in-memory key-value store from scratch, which sounds straightforward until you're actually in it and realize how many corners you have to defend.
  • Design a low-level in-memory key-value store backed only by a hash map. Define the API, specify edge case behavior, handle concurrency, and discuss the inherent limitations of this approach.

“I started with get/put/delete and felt pretty good about it, then they pushed on contains and size and I realized I hadn't thought through atomicity at all.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

Apr 2026
LinkedIn system design round for a software engineer role, focused on distributed systems. The question built on a single-machine data structure problem and then pushed into sharding, routing, and global randomness territory.
  • You've designed a randomized multiset on a single machine. Now scale it across multiple servers, keeping insert, remove, and getRandom working correctly. Walk through request routing, data partitioning, rebalancing when nodes change, and how getRandom returns a truly globally random element across all shards.

“The getRandom part is where I got tripped up.”

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LinkedIn·Software Engineer·Technical Phone Screen

Apr 2026
LinkedIn SWE interview with a calendar scheduling design problem. The question had a clean algorithmic core but the follow-up about optimizing to O(log N) with a segment tree is where things got real.
  • Design a meeting scheduler with two operations: one to record a booking given a start and end time, and another to find the earliest available slot starting from a given time that fits a requested duration without overlapping any existing booking. How do you optimize the lookup?

“I got the brute force pretty fast, keep a list, scan everything.”

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