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LinkedIn interview notes, page 11 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 software engineer interview that was basically one big question disguised as a conversation. You pick your highest-impact project and then they just... keep pulling on the thread for however long they feel like.
  • Walk me through the project you've worked on that had the largest measurable impact, including a diagram of the system architecture and data flow.
  • What specific trade-offs did you consider when making key technical decisions on that project, and what alternatives did you reject?
  • What were the concrete impact metrics for this project, such as latency improvements, cost savings, or adoption numbers?

“The whiteboard part is what gets people.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn technical phone screen for a software engineer role, pretty much a Java fundamentals deep dive. One question but they really wanted you to go all the way down the rabbit hole on it.
  • What are the differences between final, finally, and finalize in Java? Cover how each behaves, edge cases, and what the best practices are.

“I knew the basics going in but the edge cases tripped me up.”

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LinkedIn·Software Engineer·Technical Phone Screen

May 2026
LinkedIn SWE interview with a classic dynamic programming problem. Nothing too surprising but it's one of those questions that sounds easy until you're actually coding it under pressure.
  • Find the maximum product of a contiguous subarray within an integer array.

“Blanked for a second because I kept thinking about maximum subarray sum (Kadane's) and tried to apply the same logic directly.”

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LinkedIn·Data Scientist·Onsite - System Design / Architecture

May 2026
LinkedIn data scientist interview with a meaty system design question on short-video recommendations. The scope was pretty wide, covering everything from labels and features to offline evaluation and online guardrails, so you really had to show both ML chops and product thinking at the same time.
  • Design a recommendation system for a short-video feed at scale, covering the full pipeline from objectives and labels through to online evaluation and guardrails.

“This question is basically six questions stitched together and the interviewer will pull on whichever thread they want, so pacing yourself is genuinely hard.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

May 2026
LinkedIn PM interview with a classic metrics/troubleshooting question about mobile installs dropping. Pretty short session from what I could tell, just the one scenario but it went pretty deep.
  • LinkedIn's iOS app installs are down. How do you investigate and respond?

“I jumped straight into the metrics funnel which felt right but I skipped clarifying the timeframe and magnitude first, which I think cost me points early on.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn system design follow-up, the kind where they take a problem you already solved and ask what happens when you scale it by a factor of a thousand. No coding, just talking through tradeoffs for about an hour.
  • You've built a BFS-based shortest-distance function over a connection graph. Now imagine it runs in production at hundreds of millions of nodes. How do you redesign it to actually work at that scale?

“This is the kind of question where you can tell they want you to keep talking and keep going deeper.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn Data Scientist SQL round, two related questions that build on each other. Pretty clean problem setup but the histogram part tripped me up more than I expected.
  • Given a table of article views and a table of articles with their types, write a query to find, for each user, how many distinct article types they viewed on a specific date.
  • Using the result from the previous query, build a histogram showing how many users viewed exactly 1 article type, exactly 2 types, and so on, for that same date.

“Straightforward join and count distinct, nothing fancy.”

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LinkedIn·Data Scientist·Technical Phone Screen

May 2026
LinkedIn data scientist interview with a SQL-heavy problem involving notification state reconstruction across two tables. The follow-up questions pushed into schema design and logic theory territory, which I wasn't fully prepared for.
  • Given a snapshot table of members' notification settings as of January 31st and a table of toggle actions taken during February, write a query to return each member's current notification status as of February 29th. Members may appear in one or both tables.
  • If the actions table had no date column and you couldn't order the actions chronologically, could you still determine each member's final notification status? What would need to change?
  • Now assume every action in the table is a valid state transition (no repeated or contradictory actions). Without any date column, can you infer the final status from just the snapshot and the count of February actions per member?

“The full outer join part tripped me up a bit.”

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LinkedIn·AI Engineer·Onsite - Multi Round

May 2026
LinkedIn AI Engineer loop with a combined technical round: half ML fundamentals, half system design. The split felt pretty balanced but the system design portion was where I felt most exposed.
  • Walk me through common loss functions and when you'd choose one over another.
  • How do regularization techniques work and what problems are they solving?
  • Explain the bias-variance tradeoff and how it shows up in practice.
  • What are the tradeoffs between classical ML approaches and deep learning, and how do you decide which to use?
  • How does the transformer architecture work, and what role does attention play in it?
  • How do embeddings work and how are they used in ML systems?
  • What evaluation metrics would you use for an ML model and how do you choose between them?
  • Design an end-to-end ML system relevant to LinkedIn's domain, covering data ingestion, feature engineering, model training, serving, monitoring, and A/B testing.

“Pretty standard but easy to fumble if you go on autopilot.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

May 2026
LinkedIn EM interview with a system design question around building a ranked cache. Pretty open-ended, which I wasn't fully prepared for.
  • Design and implement a ranked cache system.

“I jumped straight into LRU and they pushed back almost immediately, asking why ranking specifically.”

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