How LinkedIn Interviews in 2026: Process, Questions & What They Score

• Two Sum (or variants requiring a hash-map based approach)
• Word Break / Word Break II (string segmentation via dynamic programming)
• Design and implement an LRU Cache
• Merge overlapping intervals / merge sorted arrays or lists
• Sparse Matrix Multiplication (a sparse-data problem that shows up on LinkedIn's LeetCode company-tagged list; don't confuse it with the similarly-named 'Dot Product of Two Sparse Vectors,' which is more consistently attributed to Meta/Facebook)
• Repeated DNA Sequences / Isomorphic Strings (hash-set and mapping problems)
• Graph traversal problems (e.g., shortest path or connectivity in a social-graph-style structure)
• In the newer AI-enabled coding round: LRU/LFU-cache-style, interval-merging, or structured data-transformation problems solved collaboratively with an in-IDE AI assistant, followed by production-concerns follow-ups (concurrency, multithreading, edge cases, scaling)

• Write a query using joins and aggregation to compute a user engagement metric (e.g., visits or connections over a trailing period)
• Explain the difference between filtering in an ON clause versus a WHERE clause in a join, and how it affects results
• Write a query to deduplicate records or find the most-recent/most-active record per user
• Use window functions to rank users or events (e.g., rank users by activity within a time window)
• Given login/session or job-application event data, write a query to test a specific hypothesis (e.g., whether job-switching frequency correlates with faster promotion to manager)
• Design a query to build a 360-degree view of a user's behavior across multiple events (e.g., payments and site visits)

• Design LinkedIn's News Feed (personalization, ranking by relevance/freshness/engagement, real-time aggregation at scale)
• Design a Notification System capable of high-throughput, near-real-time delivery
• Design a 'People You May Know' or job-recommendation system
• Design a typeahead/search system for a professional network
• Design a generic large-scale system such as Netflix or a rate limiter (used to test general distributed-systems fundamentals, not LinkedIn-specific knowledge)
• Follow-up trade-off discussions: pull vs. push feed generation models, caching strategy, and consistency vs. freshness trade-offs

• Tell me about yourself
• Why do you want to work at LinkedIn?
• Tell me about a time you disagreed with a teammate or manager and how you resolved it
• Tell me about a time you led a project or initiative without formal authority
• Tell me about a time you failed at something and what you learned from it
• Describe a project you're most proud of and your specific contribution
• Tell me about a time you had to adjust to a colleague's working style to get something done
• (Craftsmanship round, senior/staff levels) Tell me about a time you fell short on code quality or engineering practice: what would you do differently, and what is your approach to code review and test coverage?

• As PM for LinkedIn's Trust team, senior influencers are complaining about excessive spam connection requests and threatening to leave the platform: what do you do?
• Design a feature to help LinkedIn source and hire remote workers for software/IT companies
• As PM of the LinkedIn feed, would you prioritize 'Sponsored Ads' or 'People You May Know' in the top-right/prominent placement, and why?
• How would you measure engagement on LinkedIn Groups?
• Application volume for a product spiked unexpectedly last month: how would you investigate the root cause?
• An A/B test shows a new feature increased conversion overall, but results vary significantly by region: how would you investigate further?
• Design a preliminary-screening interview product for a recruitment firm

LinkedIn's process has 7 stages, in order: Resume/Application Screen, Online Assessment (role-dependent), Recruiter Phone Screen, Technical Phone Screen (technical roles) / Hiring Manager Screen (non-technical roles), Virtual Onsite / Onsite Loop, Team Matching, Decision, Offer & Negotiation.

LinkedIn evaluates candidates against No publicly documented, named scored-competency framework (unlike Amazon's Leadership Principles or McKinsey/BCG/Bain's PEI); LinkedIn instead publishes company-wide cultural values that multiple interview-prep sources say inform the behavioral/culture-fit round informally: We put members first, We trust and care about each other, We are open, honest and constructive, We act as One LinkedIn, We embody diversity, inclusion and belonging, Cultural mantra: "We Dream Big, Get Sh*t Done, and Know How to Have Fun".

LinkedIn's question bank spans 5 categories: Coding / Data Structures & Algorithms (SWE, DS); SQL & Data Manipulation (SWE, DS); System Design (SWE, mid-level and above); Behavioral / Culture & Craftsmanship Fit (all roles); Product Sense & Case Questions (Product Manager roles).

This playbook is high confidence, compiled from 21 public sources, and last verified July 7, 2026. It describes one well-documented shape of LinkedIn's interviews, not a guarantee of what any individual loop will look like.