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LinkedIn interview notes, page 8 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·Machine Learning Engineer·Technical Phone Screen

May 2026
LinkedIn ML engineer round, pretty technical. The whole session was basically one big gradient descent question that kept branching into trade-offs I hadn't fully thought through.
  • Walk through how batch gradient descent, stochastic gradient descent, and mini-batch SGD each compute gradients and update parameters. What are the compute and memory costs per step, and how does update noise differ across the three?
  • Is a larger batch size always better? Talk through the trade-offs across hardware utilization, convergence speed, gradient noise, and generalization.

“Started fine, got through the mechanics okay.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at LinkedIn with a couple of product sense questions centered on Spotify. Pretty standard stuff but the CEO angle on the second question tripped me up a bit.
  • How would you improve Spotify?
  • If you were the CEO of Spotify, would you build the feature you just proposed in-house? Why?

“Went with the usual breakdown: users, pain points, prioritization.”

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LinkedIn·Software Engineer·Onsite - Coding / Algorithms

May 2026
LinkedIn coding round, probably backend or infra-adjacent given the problem. One question but it had enough layers to eat up the whole session.
  • Given a set of deleted file paths and a list of services each with a read and write path, determine which services are affected. A service is affected if its path or a parent directory of its path overlaps with any deleted path. Then convert the full set of paths into a file-tree structure and return only the affected subtrees, excluding sibling subtrees that share the same root but aren't actually affected.

“The first part felt manageable, prefix matching on paths.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview that came down to a classic dynamic programming problem. Nothing too exotic, but they wanted you to know more than one way to solve it.
  • Given an integer array, find the contiguous subarray with the largest sum and return that sum. Be prepared to walk through both a linear-time solution and a divide-and-conquer approach.

“I knew Kadane's cold so that part was fine.”

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LinkedIn·Machine Learning Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn MLE interview that went deep into NLP and data mining territory. The main question was a full system design for how LinkedIn's Skills feature actually works under the hood, and they wanted you to build it from scratch, not just wave your hands at a language model.
  • Design the full ML pipeline for LinkedIn's Skills feature as a data-mining problem: how would you collect candidate skill tokens, parse and tokenize resumes, construct training data with positive and negative examples using weak supervision, train a classifier to predict whether a token is a skill, evaluate it, and keep the skill taxonomy updated over time?

“This one sprawled in every direction and I kept second-guessing where to start.”

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LinkedIn·AI Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round at LinkedIn for an AI Engineer role. Just one question but it had a lot of surface area, basically a full story about owning a quality initiative from start to finish.
  • Tell me about a time you led an initiative to improve quality, such as code health, test coverage, reliability, or customer-facing metrics. What drove you to prioritize it, how did you scope and execute it, who did you involve, and what measurable outcome came from it?

“This question has a lot of moving parts and I underestimated how much ground it covers.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview that came down to a single meaty data structure design problem. The question had layers to it and the conversation went pretty deep into trade-offs, which I wasn't fully prepared for.
  • Design a SumTable class for a 2D matrix that supports two operations: querying the sum of any rectangular submatrix, and updating the value at a specific cell. Walk through the trade-offs between different implementation approaches.

“I started with the prefix sum approach because it felt clean.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
Product design question at LinkedIn, one of those furniture-for-Airbnb prompts that sounds weirdly specific but is really just testing whether you can structure an open-ended design problem. Short session, not much else to report.
  • Design a furniture product for Airbnb.

“I blanked for a second because I kept thinking about actual furniture instead of the product angle.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn system design screen, one question about URL shorteners and how they work end to end. Pretty lean content but the question itself has some depth if you actually dig into the redirect flow.
  • Walk me through what happens behind the scenes when you enter a shortened URL (like a Bitly link) into your browser and hit enter.

“I started with DNS and HTTP basics, which was fine, but I underestimated how much they wanted on the redirect mechanics specifically.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
LinkedIn PM interview, got hit with a product strategy question about where the platform should go next. Not a lot of context to go on, just the one question.
  • What should LinkedIn build next?

“Wide open question with no guardrails, which sounds fun until you're actually sitting there.”

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