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 20 results
Role
LinkedIn logo
LinkedIn
Medium
Software Engineer

Count islands and distinct shapes

Question LeetCode 200. Number of Islands LeetCode 694. Number of Distinct Islands https://leetcode.com/problems/number-of-islands/description/ https:/...

Coding & Algorithms
4
0
20 people solved
Jul 29, 2025
LinkedIn logo
LinkedIn
Medium
Machine Learning Engineer

Find shortest word transformation with caching

Find shortest word transformation with caching You are given a start word and an end word of equal length, and a dictionary of valid words. In one mov...

Coding & Algorithms
10
0
95 people solved
Jul 16, 2025
LinkedIn logo
LinkedIn
Medium
Data Scientist

Identify and Flag Bot Traffic in Online Forum

PVE +----------+-----------+ | memberId | timestamp | +----------+-----------+ | 101 | 169100123 | | 102 | 169100225 | | 101 | 16910030...

Data Manipulation (SQL/Python)
83
0
263 people solved
Jul 12, 2025
LinkedIn logo
LinkedIn
Medium
Data Scientist

[SQL] Job Ad Metrics with Applicant Filter

Job Ad Metrics Analysis Task Table Structure Table: job_activity job_id INT candidate_id INT activity_type VARCHAR -- can be 'view' or 'apply' Requi...

Data Manipulation (SQL/Python)
11
0
44 people solved
Apr 23, 2025
LinkedIn logo
LinkedIn
Medium
Software EngineerSenior+

Design a Randomized Multiset

Implement a data structure for integers that allows duplicate values and supports the following operations in expected O(1) time: - insert(val): Add o...

Coding & Algorithms
3
0
43 people solved
Apr 2, 2026
LinkedIn logo
LinkedIn
Easy
Software Engineer AI Locked

Debug Queues and Solve Arrays

This multi-part question evaluates competencies in data structures (heap invariants and in-place array manipulation), concurrent programming (designin...

Coding & Algorithms
7
0
96 people solved
Mar 10, 2026
LinkedIn logo
LinkedIn
Medium
Machine Learning Engineer

Sample index from weighted probability distribution

Given an array weights[0..M-1] representing a discrete distribution over M outcomes, implement a function sampleIndex(weights) that returns an index i...

Coding & Algorithms
4
0
77 people solved
Feb 18, 2026
LinkedIn logo
LinkedIn
Medium
Software EngineerSenior+

Solve Cache, Window, and Heap Problems

You may be asked to solve multiple algorithmic problems in coding rounds: 1. Frequency-based cache: Implement a cache with get(key) and put(key, value...

Coding & Algorithms
0
0
13 people solved
Feb 16, 2026
LinkedIn logo
LinkedIn
Medium
Software Engineer

Design O(1) insert/delete and frequency-weighted random

Problem Design a data structure that supports the following operations in average \(O(1)\) time: 1. add(x) -> bool - Inserts value x into the colle...

Coding & Algorithms
14
1
199 people solved
Feb 12, 2026
LinkedIn logo
LinkedIn
Medium
Software Engineer

Check perfect square using binary search

Perfect Square Check (Binary Search) Given a positive integer n, determine whether it is a perfect square (i.e., there exists an integer x such that x...

Coding & Algorithms
10
2
145 people solved
Feb 11, 2026
LinkedIn logo
LinkedIn
Medium
Machine Learning Engineer Locked

Sample index from probability distribution

This question evaluates proficiency in randomized algorithms and probability-based sampling, along with algorithmic preprocessing and data-structure d...

Coding & Algorithms
31
0
217 people solved
Feb 11, 2026
LinkedIn logo
LinkedIn
Medium
Software Engineer

Compute inverse-depth weighted sum of nested lists

Inverse-Depth Weighted Sum of a Nested List You are given a nested list of integers. Each element is either: - an integer, or - another nested list. D...

Coding & Algorithms
19
1
126 people solved
Feb 11, 2026
LinkedIn logo
LinkedIn
Easy
Data Scientist

Find top countries by population per continent

Table world_population - continent VARCHAR - country VARCHAR - population BIGINT Assume each row is a country’s latest population and (continent, coun...

Data Manipulation (SQL/Python)
4
0
64 people solved
Feb 1, 2026
LinkedIn logo
LinkedIn
Easy
Data Scientist

Compute article-type diversity per user and histogram

You track article views and article metadata. Tables article_views - user_id INT - article_id INT - view_date DATE articles - article_id INT (PK) - ar...

Data Manipulation (SQL/Python)
5
0
40 people solved
Feb 1, 2026
LinkedIn logo
LinkedIn
Medium
Software Engineer

Design a stack with max removal

Design a stack-like data structure for integers that supports the following operations efficiently: - push(x): insert x onto the stack. - pop(): remov...

Coding & Algorithms
4
0
53 people solved
Jan 1, 2026
LinkedIn logo
LinkedIn
Medium
Software Engineer

Implement an LRU cache with follow-ups

Coding: Implement an LRU Cache and discuss concurrency Design and implement an in-memory Least Recently Used (LRU) cache data structure. The cache sho...

Coding & Algorithms
15
0
207 people solved
Dec 8, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Count connected land components in a grid

You are given a 2D grid of characters where: - '1' represents land - '0' represents water A group of land cells forms an island if they are connected ...

Coding & Algorithms
9
0
85 people solved
Nov 21, 2025
LinkedIn logo
LinkedIn
Medium
Software Engineer

Validate parentheses with one or three bracket types

Problem Given a string s consisting only of bracket characters, determine whether it is valid. A string is valid if: - Every opening bracket has a cor...

Coding & Algorithms
5
1
65 people solved
Oct 20, 2025
LinkedIn logo
LinkedIn
Medium
Data Scientist

Implement fast sampling for weighted k-sided die

You must sample from a categorical distribution over k outcomes with probabilities p1..pk (sum to 1) without using built-in categorical samplers. You ...

Coding & Algorithms
4
0
89 people solved
Oct 13, 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

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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