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LinkedIn interview notes, page 17 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·Hiring Manager Screen

May 2026
One-on-one with a senior hiring manager at LinkedIn for a software engineering role. The format mixed behavioral storytelling with some pretty specific customer-experience incident scenarios, which I wasn't fully expecting going in.
  • Tell me about a time you demonstrated leadership or took ownership of something that wasn't strictly your responsibility.
  • Describe a situation where you had to navigate a conflict with a teammate or stakeholder. How did you handle it?
  • Walk me through how you handled a serious customer-impacting incident from start to finish, including how you detected it, mitigated it, communicated with customers, and prevented it from happening again.
  • How do you balance empathy for customers with the practical constraints of engineering, like time, cost, or technical debt?
  • Tell me about a time you had to make a decision with incomplete information or significant ambiguity.

“I had a decent story ready but I rambled a bit at the end trying to explain the long-term impact.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data science interview with a meaty open-ended question about comparing video posting behavior across member segments. No fluff, just one big analytical problem they wanted you to work through end to end.
  • You suspect US members upload videos at a different rate than non-US members. How would you design an analysis to test this fairly, including what metrics you'd use, how you'd handle confounders like membership size and tenure, and how you'd actually run the numbers?

“This one had a lot of layers and I underestimated how long it would take to even get to the SQL part.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
Interviewed for a PM role at LinkedIn, just the one question I can remember clearly. Pretty standard prioritization territory but it still tripped me up a bit.
  • How would you prioritize competing requests coming in from multiple different teams?

“I went straight to impact and effort, which felt right in the moment, but I think I skipped over the part where you actually have to manage the relationships involved.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data scientist interview with a stats-heavy scenario question about conflicting A/B test results across two cities. Pretty classic setup but the depth they expected was real.
  • An A/B test ran in two separate cities. In each city individually, variant A outperforms variant B. But when you combine the data from both cities, variant B looks better overall. How do you make sense of that, what decision do you make, and what additional analysis would you run before rolling anything out?

“This is Simpson's paradox dressed up in a product scenario and I knew it the second I read it, which almost made it worse because I rushed into the explanation without thinking through the decision layer.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML engineer role at LinkedIn and got a statistics question. Pretty bare bones from what I can tell, not much else to go on.
  • Walk me through your experience with statistics.

“Broad question and I kind of rambled.”

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LinkedIn·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Behavioral round for a Data Engineer role at LinkedIn. One question, but it had enough moving parts that I was still thinking about my answer on the drive home.
  • Tell me about a time you had to step away from a project where you were the main person who understood how everything worked. How did you make sure things didn't fall apart while you were gone?

“I had a decent story for this but I front-loaded too much context about why I was the only one who knew the system, and by the time I got to what I actually did, I was rushing.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data scientist interview that went deep into the math. They wanted a full derivation of backpropagation on the whiteboard, not just a conceptual walkthrough, which I was not fully prepared for.
  • Derive backpropagation from scratch for a feed-forward neural network. Walk through the forward pass equations for each layer (linear transformation plus activation), define the loss function, apply the chain rule to get the gradients of the loss with respect to weights and biases at each layer, and write out the parameter update rule. Math notation required, not just verbal explanation.

“I knew backprop conceptually but writing out the actual partial derivatives layer by layer under pressure is a different thing.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
LinkedIn product interview, one question about redesigning the onboarding experience. Not much context to go on but it's a classic LinkedIn PM prompt.
  • How would you improve LinkedIn's onboarding flow?

“This is one of those questions that sounds approachable until you realize how many user types LinkedIn actually has.”

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LinkedIn·AI Engineer·Technical Phone Screen

May 2026
LinkedIn AI Engineer technical screen, one meaty probability/sampling question that took up most of the time. Pretty focused session, no fluff.
  • You have an N-sided die where each face has a probability derived from a softmax over a logits vector. Implement an efficient sampler that returns a face index according to those probabilities. Walk through your approach and any alternatives you know.

“I went straight for cumulative sum plus binary search, which is the standard inverse-CDF move.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data scientist interview focused entirely on the job recommendation algorithm upgrade. Four questions, all connected, all requiring you to think end-to-end about experimentation and metrics. Not a casual screen.
  • What offline and online metrics would you use to evaluate whether a new job recommendation algorithm is actually performing better?
  • How would you design an A/B test to compare the new recommendation model against the existing one, specifically accounting for network effects between users?
  • How do you figure out the right sample size, how long to run the test, and what percentage of traffic to expose to the new model?
  • If the new model performs well for some user segments but poorly for others, how would you figure out why and what would you do next?

“I went straight to online metrics (click-through rate, apply rate) and almost forgot to talk about offline evaluation entirely.”

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