LinkedIn Interview Questions

LinkedIn Interview Questions

Practice 147 real LinkedIn interview questions for 2026. Covers all top categories — Coding & Algorithms, System Design, Analytics & Experimentation, Data Manipulation (SQL/Python), and Behavioral & Leadership — across Software Engineer, Data Scientist, Machine Learning Engineer, and Data Engineer roles. Real questions from actual interviews with detailed solutions; this collection is designed for focused interview preparation that prioritizes coding and scale-first problem solving alongside rigorous metrics thinking. Expect LinkedIn interviews to evaluate production-ready tradeoffs, clear metricization of ranking and relevance, and the ability to diagnose live-traffic regressions. For Software Engineer candidates, recurring themes include constant-time randomized data structures and frequency-weighted sampling, Top-K ranking service design and distributed-scaling considerations, plus classic array/string and stack-with-max algorithmic problems. Data Scientists should be ready for model fundamentals and optimization (logistic regression, backprop, Adam), causal and experimentation diagnostics for feed/homepage drops, and sampling/variance concerns in ranking metrics. Machine Learning Engineers will see recommendation and skills-inference system design, clustering convergence and probabilistic sampling questions, and production alerting/spike-detection. Data Engineers encounter efficient data-structure implementations tied to measurable production impact.

147 Questions 1 Company06.23.2026
Showing 7 results
Role
LinkedIn logo
LinkedIn
Medium
Software Engineer

Detect intersection of two linked lists

Given two singly linked lists that may converge to share a common tail, determine whether they intersect and return the first node at which they join....

Coding & Algorithms
4
0
59 people solved
Sep 6, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Solve six algorithmic problems

Answer the following independent algorithmic prompts. For each, explain your approach, justify data structures, analyze time/space complexity, and pro...

Coding & Algorithms
7
0
135 people solved
Sep 6, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Reverse a linked list

Question LeetCode 206. Reverse Linked List — Given the head of a singly linked list, reverse the list and return the reversed list. https://leetcode.c...

Coding & Algorithms
5
0
18 people solved
Jul 29, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Find lowest common ancestor

Question LeetCode 236. Lowest Common Ancestor of a Binary Tree — Given a binary tree, find the lowest common ancestor (LCA) of two given nodes. Follow...

Coding & Algorithms
8
0
35 people solved
Jul 29, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Solve min window & animal conflicts

Question LeetCode 76. Minimum Window Substring: Given strings s and t, return the smallest substring of s that contains every character of t (includin...

Coding & Algorithms
21
0
54 people solved
Jul 29, 2025
LinkedIn logo
LinkedIn
Medium
Machine Learning Engineer

Compute total covered interval length

Compute total covered interval length Given a list of integer intervals [l, r) (half-open), compute the total length covered by at least one interval....

Coding & Algorithms
13
0
106 people solved
Jul 16, 2025
LinkedIn logo
LinkedIn
Medium
Data Scientist

Identify Top Contributors by Recent Post Count

posts +----+---------+---------------------+ | id | user_id | created_at | +----+---------+---------------------+ | 1 | 101 | 2023-09-01...

Data Manipulation (SQL/Python)
63
0
4 people solved
Jul 12, 2025

Frequently Asked Questions

How difficult are LinkedIn interview questions?
LinkedIn interview difficulty varies by role and level but is generally medium-to-hard for technical roles. For software engineers expect algorithmic coding problems that are often LeetCode-medium-to-hard complexity, plus harder system design for senior levels. Data scientist and machine learning engineer rounds emphasize applied statistics, experiment design, model evaluation, and production ML system tradeoffs rather than purely theoretical proofs. Data engineer and analytics roles focus on scalable data pipelines, SQL performance, and practical ETL challenges. Interviewers expect clear thinking under time pressure, scalable designs, and strong communication; failing to justify tradeoffs or to explain assumptions is the most common failure mode.
What is the LinkedIn interview process and where do these 147 LinkedIn interview questions appear?
LinkedIn hires through a staged process starting with a recruiter screen, a technical phone screen or online assessment, then a virtual onsite loop of four to five interviews that combine coding, system or ML design, and behavioral rounds. The 147 questions in this set map across Data Scientist, Software Engineer, Machine Learning Engineer, and Data Engineer tracks: expect coding and data-structure problems in software engineering rounds, SQL and experiment/statistics problems in data science rounds, ML system and recommendation design for MLE roles, and pipeline and performance problems for data engineers. Senior candidates face heavier design and leadership evaluation.
How should I structure a preparation timeline to cover 147 LinkedIn interview questions before my onsite?
Plan a 6–10 week timeline depending on your starting level. Spend weeks 1–2 refreshing fundamentals: data structures, SQL, probability, and core ML concepts. Weeks 3–6 alternate focused practice blocks for each role represented in the 147 questions: two days of coding problems, one day of systems or modeling design, and one day of SQL/experimentation work per week. Reserve the final 1–2 weeks for timed mock interviews, behavioral STAR stories, and reviewing weak spots. Spread practice across real interview-like conditions with a shared editor for coding and a whiteboard or doc for designs.
What are the key subtopics I should prioritize across positions at LinkedIn?
Prioritize role-specific themes revealed by actual question titles: for data scientists focus on logistic regression and backprop intuition, causal experiment design, diagnosing traffic or feed relevance drops, imbalance handling, and variance reduction methods in ensembles. For software engineers prioritize efficient randomized data structures, O(1) insert/delete, top-K ranking services, common string and subarray algorithms, and debugging distributed queues. Machine learning engineers should emphasize recommender-system design, feature sampling and weighted-index sampling, K-means convergence, and practical ML pipeline monitoring. Data engineers should demonstrate robust stack implementations and measurable impact from pipeline projects.
Any standout tips and common pitfalls for LinkedIn interviews I should watch for?
Emphasize clear assumptions, measurable success metrics, and tradeoffs in every design or analysis answer. For data roles always tie model or experiment choices back to business metrics and experiment validity; avoid asserting causation without addressing confounders. For coding, prioritize correctness with clear complexity analysis and then optimize incrementally. In system and ML design, call out data pipelines, monitoring, alerting, and rollback strategies. Common pitfalls include vague requirements, skipping edge cases, ignoring scale and reliability, and weak communication; rehearse concise explanations and have two to three strong impact stories ready.

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