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LinkedIn interview notes, page 13 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 SWE interview, one coding question on tree serialization. Short and to the point, nothing fancy.
  • Implement serialize and deserialize functions for a binary tree.

“Classic problem but I still fumbled the deserialization part a bit.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn DS interview focused entirely on a machine learning case study around classifying sales professionals on the platform. Four connected questions that built on each other, which I wasn't fully expecting going in.
  • What features and data sources would you use to build a classifier that identifies sales professionals on LinkedIn?
  • How would you establish ground truth labels for this classifier, and how would you handle class imbalance?
  • Which modeling approaches would you start with for this classification problem, and what's your reasoning?
  • How would you evaluate this model offline and then validate it once it's live?

“I jumped straight to profile-based signals (job title, industry, connections in sales roles) and forgot about behavioral stuff entirely for like two minutes.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
LinkedIn PM interview, just one question about a product you like. Pretty low-key as far as interviews go.
  • What is your favorite digital or physical product, and what makes it stand out to you?

“Classic opener but it can trip you up if you pick something too obscure or too obvious.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data scientist interview with a deep technical question on sampling algorithms. Single question but it had five sub-parts and felt more like a system design exercise than a coding screen. Came out of it feeling like I'd only really nailed two of the five parts.
  • Design an algorithm to sample from a categorical distribution over k outcomes using only a Uniform(0,1) random number generator, with O(k) preprocessing and O(1) sampling time. Walk through the build and sample logic, complexity, floating-point edge cases, support for incremental probability updates, and a statistical validation plan.

“I knew the alias method from a stats course a few years back so the core pseudocode came out okay.”

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LinkedIn·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Two rounds at LinkedIn for a software engineering role, one with a manager and one focused on communication. Both were behavioral and system-oriented, no coding involved. Pretty straightforward if you've done this kind of interview before.
  • Tell me about a time you had to resolve a complex issue that involved multiple teams or stakeholders. What did you do to get everyone aligned and push it to a conclusion?
  • Pretend I'm starting on your team next week. Walk me through the system your team owns, sketch out the architecture, and tell me how you'd get me up to speed fast.

“This one I actually felt okay about.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026
LinkedIn AI Engineer interview with a probability and randomness problem that sounds approachable until you're actually in it. The core challenge was building a fair uniform distribution over 7 values using only a biased coin you know nothing about.
  • You have a function that returns 0 with unknown probability p and 1 with probability 1-p. Using only this function, write code that returns each integer from 0 to 6 with equal probability.

“I knew the Von Neumann trick from somewhere in the back of my brain, pairs of calls where 01 maps to 0 and 10 maps to 1 and you just retry on 00 or 11.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview with a recursive array problem. Pretty standard algorithmic question but the complexity discussion at the end is where things got interesting.
  • Given a nested array of arbitrary depth where elements can be numbers or further nested arrays, write a function that sums all numeric values across all levels. For example, sum([[1, 2], [3, [4, 5]], 6]) should return 21. You can use recursion or an explicit stack. Also discuss time and space complexity in terms of total element count and maximum nesting depth.

“The coding part was fine, recursion felt natural here and I got it working pretty quickly.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE coding round, pretty standard heap problem but they threw in some follow-ups that made it more interesting than I expected.
  • Given two sorted integer arrays and an integer k, return the k pairs (one element from each array) with the smallest sums.

“Got to the min-heap approach pretty quickly, seeding with the first element of nums1 paired with each element of nums2, then expanding lazily.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview with a 2D matrix problem requiring both point updates and range sum queries. Pretty classic data structures territory but the efficiency requirement is what makes it interesting.
  • Given a 2D integer matrix and a series of queries, implement a system that handles two operations efficiently: updating a single cell's value, and returning the sum of all elements within a rectangular submatrix defined by half-open row and column ranges.

“The naive approach works fine for small inputs but you know they're looking for something smarter.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn SWE interview focused on system design, specifically building a typeahead autocomplete for person search. Pretty classic infra problem but the LinkedIn-specific constraints make it more interesting than it sounds.
  • Design a typeahead autocomplete system for person search on LinkedIn.

“I started with the obvious stuff, trie-based prefix matching, caching hot queries, but the LinkedIn angle tripped me up a bit.”

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