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LinkedIn interview notes, page 20 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·Mobile Engineer·Technical Phone Screen

Jun 2026
LinkedIn mobile engineer interview with a weighted nested list problem. Pretty standard algorithmic round but the inverse-depth weighting twist made me second-guess myself more than I'd like to admit.
  • Given a nested list of integers where each element can be an integer or another nested list, compute a weighted sum where each integer's weight is (maxDepth - depth + 1). Deeper integers get lower weight.

“I initially coded the straightforward version where deeper elements get more weight, which is the classic problem.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn SWE interview with a classic array problem. Nothing too surprising but the follow-up on time complexity is where things get interesting.
  • Given an integer array and an integer k, find the kth largest element in the array. The kth largest is based on sorted order, not distinct values.

“I went with the min-heap approach, keep a heap of size k and just push everything through it.”

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LinkedIn·Data Scientist·Technical Phone Screen

Jun 2026
LinkedIn data scientist technical screen, pretty deep on the ML fundamentals side. They wanted you to actually derive things, not just name-drop concepts, which I was not fully prepared for.
  • Walk through logistic regression end to end: the model formulation, the loss function, deriving the gradient of the loss with respect to weights, and how gradient descent is applied with its update rule. Be ready to write the math out.

“This is where things got uncomfortable.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn software engineer interview with a sliding window problem that looked easy until the follow-up made me rethink everything I'd written.
  • Given a binary array, you can flip at most one 0 to a 1. Return the length of the longest contiguous run of 1s you can achieve. Then: can you solve it in O(1) space if the array arrives as a stream, one element at a time?

“The base problem I got pretty fast, sliding window with a counter tracking how many zeros are inside the window.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn software engineer interview that came down to a single tree problem, asked to solve it two ways. Pretty standard coding round but the dual-solution requirement tripped me up a bit.
  • Given the root of a binary tree, determine whether the tree is symmetric around its center. You need to provide both a recursive and an iterative solution.

“The recursive part came naturally.”

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LinkedIn·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Short LinkedIn interview that basically came down to one strategy question about the Glint acquisition. Not much else to go on.
  • Why do you think LinkedIn decided to acquire Glint?

“I fumbled around with the obvious stuff first, employee engagement data, HR analytics, and then tried to connect it to LinkedIn's broader play around the full employment lifecycle.”

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LinkedIn·Machine Learning Engineer·Technical Phone Screen

Jun 2026
LinkedIn ML Engineer screen, pretty heavy on ML fundamentals. One question but they really wanted you to go deep on it, not just recite a definition.
  • Why is logistic regression a good fit for binary classification, and how can you justify treating its output as a probability?

“This sounds like a warmup question until they ask you to actually derive things.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn software engineer interview with a tree problem that felt straightforward once I remembered to actually use the BST property instead of treating it like a generic binary tree.
  • Given the root of a binary search tree and two distinct nodes p and q, find their lowest common ancestor.

“My first instinct was to do some kind of path-tracking approach, storing ancestors in a set, which works but completely ignores the BST structure.”

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LinkedIn·Software Engineer·Technical Phone Screen

Jun 2026
LinkedIn SWE coding round, one question, pretty standard dynamic programming territory but the negative number wrinkle is where people trip up.
  • Given an integer array, find the contiguous subarray with the largest product and return that product.

“The naive O(n^2) approach is obvious but they want linear time.”

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LinkedIn·Software Engineer·Onsite - System Design / Architecture

Jun 2026
LinkedIn system design round for a software engineer role, basically a deep dive into building a calendar product at massive scale. The scope was enormous and I felt like I was constantly playing catch-up trying to cover everything they wanted.
  • Design a multi-tenant calendar system that supports hundreds of millions of users, covering events, attendees, invitations, reminders, recurring rules, time zones, shared calendars, and access control.
  • Define the REST or gRPC API surface for this calendar system, including endpoints for creating and updating events, listing events in a time range, inviting attendees, RSVP handling, searching, and managing calendar shares. How do you handle idempotency, pagination, and rate limiting?
  • Walk through the relational schema for this system. What tables do you need, what are the primary and foreign keys, and how do you handle soft deletes and audit fields?
  • Write SQL queries to list all events visible to a user within a time range, detect scheduling conflicts for a new event, and materialize a single occurrence from a recurring series given its recurrence rule and any exceptions.
  • How would you partition and shard the data, and what caching and search indexing strategies would you use? Should recurring event instances be expanded in the background or computed on the fly?
  • How do you handle concurrent edits to the same event, ensure idempotent retries, and manage distributed transactions? Where would you use the outbox pattern versus distributed transactions, and how do you handle eventual consistency for search and notifications?
  • How would you design the reminder and notification delivery system, support ICS import and export, and handle external calendar sync? How do you prevent abuse and enforce quotas?
  • Compare relational, document, and time-series databases for different parts of this system. What would you use as the primary store and what polyglot components would you add?

“The question is basically 'design Google Calendar but justify every decision.' I started with the data model because that felt most concrete, but the interviewer kept pulling me toward scalability before I'd even finished the schema.”

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