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LinkedIn interview notes, page 27 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

Jun 2026
LinkedIn ML Engineer interview with a coding-heavy question on stratified sampling from a JSON-structured dataset. One meaty problem that covered implementation, edge cases, and a theory discussion about sampling bias on imbalanced data.
  • Given a JSON object where each key is a class label mapping to a list of samples, implement a stratified sampling function that either samples uniformly within each class or draws N total samples with proportional class representation. The solution must be seeded for reproducibility and handle edge cases like empty classes, oversized sample requests, and single-class inputs.
  • Why is stratified sampling preferable to flat uniform sampling for imbalanced datasets, and what bias does flat sampling introduce?

“This took longer than I expected to get right.”

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LinkedIn·Data Scientist·Onsite - Product Sense / Strategy

Jun 2026
LinkedIn data scientist interview focused entirely on a B2B product case, four questions all circling the same scenario. Heavy on product analytics and strategy thinking, less about coding.
  • A new B2B product just launched at LinkedIn. How would you build a metric framework to define what success looks like for it?
  • If you're looking at metric distributions and you notice metric A is up while metric B is down, how do you investigate that?
  • How would you determine whether this new product is actually delivering real value to its users, not just surface engagement?
  • Walk me through how you'd use data to identify and prioritize growth opportunities for this product going forward.

“I went with the classic funnel breakdown: acquisition, activation, engagement, retention, revenue.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn interview with one technical behavioral question about handling complexity. Short and to the point, nothing fancy.
  • Walk me through a technically complex problem you worked on and what your specific contributions were.

“I had a decent story ready but fumbled the 'your contributions' part.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at LinkedIn, one question about measuring success for the Jobs feature. Pretty straightforward product analytics prompt but it has more layers than it looks.
  • How would you determine whether LinkedIn's Job feature is successful?

“I jumped straight to job seeker metrics, apply rates and profile views, and kind of forgot that LinkedIn's Jobs feature serves multiple sides.”

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LinkedIn·AI Engineer·Technical Phone Screen

Jun 2026
LinkedIn AI Engineer interview with a machine learning system design question centered on collaborative filtering. The problem was more implementation-heavy than I expected, pushing into data structure territory pretty quickly.
  • Given a sparse user-movie rating matrix, how would you use kNN to predict a target user's rating for a movie they haven't seen? Walk through your similarity metric choice, neighbor selection, and the data structures you'd use to make it efficient.

“I started with cosine similarity because it felt safer than Pearson and I could explain it faster under pressure.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn system design round for a software engineer role, one big question on virtual memory that went pretty deep. More OS theory than I expected for a product company, but they clearly wanted to see if you could reason through the whole stack.
  • Explain how virtual memory works, covering address spaces, page tables, the MMU and TLB, demand paging, page faults, swap, and the trade-offs involved.

“This one sprawled in ways I didn't anticipate.”

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LinkedIn·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Interviewed for a bizops role at LinkedIn, got a strategy question about building out a feature for contractors and freelancers. Pretty open-ended, which I wasn't fully prepared for.
  • What factors would you consider when developing a strategy for a new product feature targeting contractors and freelancers?

“I started by thinking about the user segment, like who exactly counts as a contractor versus a freelancer and whether LinkedIn even treats them differently.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026
LinkedIn ML engineer interview that was pretty focused on probability and sampling fundamentals. One coding question with a follow-up that tripped me up a bit.
  • Given a probability distribution over M outcomes, implement a sampler that picks an index proportional to its probability. Build the prefix sum array and write the binary search from scratch.
  • Follow-up: what do you do if the input probabilities don't sum to exactly 1.0?

“Prefix sums clicked fast but I fumbled the binary search a little.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026
LinkedIn ML Engineer interview with a couple of decision tree questions. Nothing too crazy but the second one had a lot of moving parts and I felt like I was rambling by the end.
  • For a classification tree, does a leaf node have to output exactly 0 or 1? Explain your reasoning.
  • Compare two trained trees: one where every leaf has exactly one training sample, and another where every leaf has multiple samples. Which overfits more, and why? Bring in variance, model capacity, and how regularization parameters like max depth and minimum samples per leaf relate to this.

“Easier than it sounds but I almost over-explained it.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Got a product strategy question at LinkedIn for a PM role, just the one question from what I can tell. Not much context around the round itself but the question was meaty enough to chew on for a while.
  • What criteria do you use to decide whether to build a generative AI model from scratch, fine-tune an existing one, or purchase a third-party solution?

“This one tripped me up more than I expected.”

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