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LinkedIn interview notes, page 19 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

May 2026
LinkedIn PM interview, just the one question about growing the student segment. Short session, left feeling like I probably underdid the segmentation side of things.
  • How would you grow LinkedIn's student user base?

“I went straight to acquisition and kind of forgot that retention for students is a completely different beast.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview that came down to a classic subarray problem. Nothing too exotic but the follow-up about returning indices plus the divide-and-conquer discussion made it more involved than I expected.
  • Given an integer array, find the contiguous subarray with the largest sum and return its sum. Expected O(n) time.
  • Follow-up: modify your solution to also return the start and end indices of the maximum subarray.
  • Can you walk through a divide-and-conquer approach to this problem and compare it to the linear solution?

“The core Kadane's logic came back to me pretty fast: track the max sum ending at the current position and decide whether to extend the running total or restart from the current element.”

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LinkedIn·Product Manager·Onsite - Behavioral / Leadership

May 2026
Went through a behavioral round at LinkedIn, pretty standard stuff but the product-building question tripped me up more than I expected.
  • Tell me about a time you built a product or feature that turned out really well.

“I had a story ready but I spent too long on the backstory and barely got to what made it actually excellent.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jun 2026
LinkedIn data scientist interview with a meaty multi-part question about training a tree-based model on imbalanced, large-scale data. The whole thing was one big scenario broken into five sub-questions, which felt more like a case interview than a typical ML screen.
  • When the positive class is rare in a large dataset, how would you handle class imbalance during training?
  • If you're training on a sample of a very large dataset, how do you confirm the sample actually represents the full population?
  • How would you validate that a model trained on a sample generalizes to the full dataset, not just to a held-out portion of the sample?
  • What techniques would you use to prevent overfitting in a tree-based model?
  • With highly imbalanced classes, which evaluation metrics would you choose and why?

“I went with the usual suspects: oversampling the minority class, undersampling the majority, or adjusting class weights in the model.”

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LinkedIn·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
LinkedIn software engineer interview that leaned hard into complexity analysis. Not just 'what's the Big-O' but justify it, compare alternatives, talk trade-offs. More rigorous than I expected.
  • For an algorithm you implement, analyze its time and space complexity in full: derive best, average, and worst cases, identify the dominant operations, explain how it scales with input size, compare at least one alternative approach with trade-offs, and describe any optimizations you'd apply.

“This is way more than the usual 'state the Big-O and move on.' They wanted me to actually derive it and justify each case separately, not just say O(n log n) and shrug.”

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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 recommendation systems. It was a discussion-style interview where they pushed hard on trade-offs at every layer rather than just wanting a diagram.
  • Walk through the end-to-end architecture of a recommendation system, covering data sources, feature engineering, candidate generation, ranking, re-ranking, and evaluation. How does it evolve from a simple popularity-based system to a personalized one?

“This is one of those questions that looks manageable until they start asking why at every single step.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn infrastructure interview, one technical coding round focused on a data structure design problem. The question was a step up from the classic version and I wasn't fully prepared for the wrinkle around duplicate handling.
  • Design a 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 their frequency.

“I knew the LC 380 version cold, array plus hashmap, swap-with-last on remove, done.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn SWE interview with a classic rotated array search problem. Pretty standard algorithmic round but the O(log n) constraint is what makes it interesting, you can't just scan linearly and call it a day.
  • You're given a sorted integer array that's been rotated at some unknown pivot. Given a target value, return its index or -1 if it's not there. All values are distinct. Must run in O(log n).

“The O(log n) requirement is basically telling you binary search, but the rotation breaks the usual assumption that one side is always smaller.”

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LinkedIn·Product Manager·Onsite - Cross-functional / Panel

Jun 2026
LinkedIn PM interview, one question about cross-team metric conflicts. Short post, not much context around the round or outcome.
  • If a feature you're building is going to hurt another team's metrics, how do you handle that?

“This is the kind of question where the wrong move is pretending the conflict doesn't exist.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jun 2026
LinkedIn Data Scientist interview focused almost entirely on experiment design for a scenario where two features launch simultaneously. Pretty technical throughout, lots of stats.
  • If marketing is running an email campaign at the same time product is shipping a new in-app messaging feature, how would you design experiments to measure each one's effect separately?
  • Walk through how you'd determine sample size, statistical power, and how long to run the test.
  • What are your options for handling interaction effects between the two features, and what are the tradeoffs between approaches like a full factorial design versus staggered rollouts?
  • How do you maintain statistical validity when you're doing sequential testing or peeking at results before the experiment ends?

“This is where I started rambling.”

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