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LinkedIn interview notes, page 22 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·Data Scientist·Technical Phone Screen

Jun 2026
LinkedIn DS interview with a product analytics case about diagnosing a drop in job application volume. One question, open-ended, and they clearly wanted to see structured thinking across multiple layers rather than just one obvious answer.
  • A job marketplace sees its daily application count declining week over week. How would you diagnose the root cause and what would you recommend?

“I jumped straight to funnel analysis which felt right but I spent too long there and almost forgot to check the obvious stuff like whether a recent release broke something.”

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LinkedIn·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at LinkedIn for a software engineering role. Two questions, both pretty standard, but the challenge one took more out of me than I expected.
  • Give a five-minute self-introduction covering your background, key projects, strengths, and career goals.
  • Walk me through a significant challenge you faced at work: what was the situation, what did you do, and what came out of it?

“I'd practiced this but still rambled past the time.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn coding screen for a software engineer role. Pretty straightforward session, just one algorithm problem and they let you think out loud.
  • Given an integer array and an integer k, find and return the k-th largest element in the array.

“My first instinct was to just sort and index from the end, which works but they pushed back on efficiency pretty fast.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn software engineer interview that was basically one meaty coding problem with a distributed systems follow-up tacked on. The coding part was manageable but the follow-up went places I wasn't fully ready for.
  • Implement a data structure that supports insert, remove, and getRandom in average O(1) time, where duplicate values are allowed and getRandom returns elements proportional to how many times each value appears.
  • How would you scale this data structure across multiple servers in a distributed setting? Walk through how each operation would work and what trade-offs you'd accept.

“I'd done the simpler version before so I felt okay starting out.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn SWE interview with a classic graph problem dressed up as a social scenario. The O(n) angle is what separates the good solutions from the brute-force ones, and I had to think carefully before I stopped fumbling around with nested loops.
  • Given n people at a party, find the 'celebrity' using only a knows(a, b) API. A celebrity is known by everyone but knows nobody. Return the celebrity's label or -1 if none exists, using as few API calls as possible.

“My first instinct was the n-squared brute force and I actually started explaining it before catching myself.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

Jun 2026
LinkedIn system design round for a software engineer role, focused entirely on designing an embedded key-value store library from scratch. The constraint of only using map-like data structures kept things interesting, and the follow-up questions on concurrency and durability made it clear they wanted depth, not just a surface-level answer.
  • Design a low-level key-value store library (like an embedded storage engine) where in-memory indexing is limited to map or dictionary-like structures. It needs O(1) average time for get, put, and delete operations.
  • How would you make the key-value store safe for concurrent access across multiple threads?
  • How would you approach durability and crash recovery for this store? What about compaction?
  • How would you add TTL or key expiration to this store, and what are the tradeoffs of different expiration strategies?
  • How would you test this key-value store library?

“This took me a second to parse because I kept thinking about distributed KV stores like Redis and had to mentally downshift to something more like RocksDB or even just a hash map with extra steps.”

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LinkedIn·Mobile Engineer·Technical Phone Screen

Jun 2026
LinkedIn mobile engineering interview with a coding problem that looked simple on the surface but had a specific constraint that tripped me up a bit. Binary search was the expected approach and they were pretty clear about the O(log n) requirement.
  • Given a positive integer, determine whether it is a perfect square without using any built-in square root functions. Your solution must run in O(log n) time.

“My first instinct was to just iterate from 1 upward and check each square, which obviously fails the time constraint.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn system design or troubleshooting round, one question about diagnosing a messaging outage. Not much else to go on from what I remember.
  • If LinkedIn's messaging feature goes down, how would you go about identifying the root cause?

“I started with the obvious stuff, check recent deploys, look at error rates, see if it's regional or global.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jun 2026
LinkedIn data science technical screen, pretty much all stats fundamentals. Six questions back to back covering queuing theory, height distributions, social network degree distributions, and regularization. Nothing crazy hard but a couple of them required more careful thinking than I expected.
  • A bank has 5 tellers. System A has all 5 sharing one common queue. System B gives each teller their own separate queue. As a customer, which would you prefer and why? Compare the two on expected wait time, variance of wait time, and fairness.
  • Describe the distribution of adult male heights in the US, and separately, the distribution of adult female heights.
  • If you pool men and women together and ignore sex entirely, what does the combined height distribution look like?
  • On a social network like LinkedIn, what does the distribution of number of connections per user look like? Is it symmetric, left-skewed, or right-skewed?
  • For that connections distribution, how do the mean, median, and mode compare? And roughly what factors would determine the scale of the mean?
  • Are L1-regularized (lasso) and L2-regularized (ridge) estimators biased? Why or why not?

“This was the most interesting question of the screen.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at LinkedIn, one question about how the platform benefits from users filling out their profiles. Pretty short interaction, not much else to report.
  • What competitive advantage does LinkedIn gain when users complete their profiles?

“I went straight to network effects and data quality, which felt right, but I think I undersold the supply side of it.”

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