LinkedIn interview questions

Showing 121–150 of 154 results

• Measure Relevant Feed SuccessHard

  This question tests the ability to define North Star metrics for a social feed and design an experimentation framework that accounts for long-term ecosystem health and cross-side network effects.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• Measure Success of New B2B ProductMedium

  Evaluates success measurement for a new LinkedIn B2B SaaS product at account and seat levels. Strong answers define activation, engagement, value, retention, expansion, anomaly analysis, and growth prioritization.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Merge Two N-ary Trees by Matching Node KeysEasy

  Merge two rooted n-ary trees in which every node has an integer key, combining nodes only when their keys match under matching parents. Tests recursive tree traversal, matching children by key, handling roots with different keys, and producing a canonical preorder encoding of the merged tree.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Merge and Serialize N-ary TreesHard

  Tests recursive tree manipulation, immutable merging semantics, and robust string serialization/deserialization with input validation.

  LinkedInSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Minimize adjacent-color assignment costMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Minimize adjacent-color assignment cost states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Minimum index distance between two given words in a word listEasy

  Coding problem: given a list of words and two different target words that both appear in it, return the smallest distance between an index of one word and an index of the other. It tests single-pass scanning with position tracking, handling of repeated occurrences, and time complexity analysis.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Plan and lead a large recommendation projectMedium

  This Behavioral & Leadership question evaluates leadership, execution planning, cross-functional coordination, resourcing, and stakeholder communication skills in the context of machine learning recommendation product development.

  LinkedInMachine Learning EngineerBehavioral & LeadershipOnsite
• Present Insights from Course Supply and Participation DataMedium

  Analyze course catalog supply and learner participation using time trends, category comparisons, utilization measures, and evidence-based presentation.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Randomized String Multiset With Average O(1) Insert, Remove and Weighted Random PickMedium

  Design a collection of strings that allows duplicates and supports insert, remove and a random pick in average constant time, where each stored occurrence is equally likely to be returned. It tests hash map and array layout, index bookkeeping on removal, and how string keys change the cost analysis.

  LinkedInSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Reduce Stale Listings in a Job MarketplaceHard

  Design an auditable staleness-detection and remediation engine for a global job marketplace that aggregates heterogeneous signals, enforces graduated interventions, and supports low-latency employer appeals.

  LinkedInSoftware EngineerSystem DesignTechnical Screen
• Resolve Conflicting A/B Test Results in CitiesMedium

  Evaluates interpretation of conflicting A/B test results across cities using Simpson's paradox and stratified estimands. Strong answers check weighting, SRM, heterogeneity, confidence intervals, and rollout criteria.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• Resolve Simpson’s paradox in A/B email testEasy

  This question tests your ability to identify and resolve Simpson's Paradox in A/B test results, where an aggregate trend disappears or reverses when segmented by user cohorts.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• Reverse a linked listMedium

  This question evaluates understanding of linked list data structures and proficiency manipulating pointers or references to perform in-place transformations when reversing a linked list. Commonly asked in Coding & Algorithms interviews, it gauges algorithmic thinking and practical application-level competency in list operations rather than purely conceptual theory.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Review a Web Application ArchitectureEasy

  This question evaluates system design competencies such as scalability, reliability, fault tolerance, database architecture, load balancing, external dependency management, and observability, and is categorized under System Design.

  LinkedInSoftware EngineerSystem DesignTechnical Screen
• Sample index from probability distributionMedium

  This question tests the ability to implement a weighted random sampling algorithm efficiently using prefix sums and binary search to achieve sub-linear query time.

  LinkedInMachine Learning EngineerCoding & AlgorithmsOnsite
• Sample uniformly from a circle’s areaMedium

  This question evaluates understanding of continuous probability distributions and geometric random sampling, testing whether a candidate recognizes how area-weighted uniformity differs from naive radial choices.

  LinkedInMachine Learning EngineerCoding & AlgorithmsOnsite
• Scale a NoSQL Key–Value Store to One Million QPSMedium

  Design a distributed, highly available NoSQL key-value store capable of serving 1M QPS across 50 million key-value pairs with low p99 latency.

  LinkedInSoftware EngineerSystem DesignOnsite
• Second-Smallest Value in a Min Tournament Tree in Logarithmic TimeMedium

  Given a full binary tournament tree stored in array order, where each internal node holds the smaller of its two children, return the second-smallest leaf value in logarithmic time. It tests reasoning about how a knockout structure constrains where the runner-up can be, array-based tree indexing, and justifying a partial search.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Shortest Substring Containing Every Character of a PatternMedium

  Return the shortest substring of a string that contains every character of a pattern with matching multiplicity, case-sensitively, choosing the leftmost substring on ties and an empty string when none exists. Tests efficient substring search and careful character-count bookkeeping.

  LinkedInSoftware EngineerCoding & AlgorithmsOther
• Shortest Word Distance QueriesHard

  Tests designing an inverted index and using two-pointer or binary search techniques to efficiently compute the minimum absolute index distance between word occurrences across repeated queries.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Single Queue vs Multiple Queues — Service DesignMedium

  Evaluates queueing-system reasoning for single-line versus multiple-line bank teller designs. Strong answers compare M/M/c pooling with parallel M/M/1 queues, waiting-time averages, variance, stability, fairness, and operations.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Solve Cache, Window, and Heap ProblemsMedium

  This question evaluates competency in advanced data structures and algorithmic techniques—specifically frequency-based cache design, sliding-window substring search, and heap-driven k-pair generation—along with time and space complexity analysis and robust edge-case handling.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Solve common string and subarray problemsMedium

  Assesses core string and array manipulation skills by requiring string reversal and contiguous-subarray computations for maximum sum and maximum product, identified under the Coding & Algorithms category.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Solve min window & animal conflictsMedium

  This question evaluates understanding of string algorithms, frequency-based sliding window techniques and hashmap/graph-based conflict detection, focusing on minimal substring search and modeling pairwise exclusions.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Solve six algorithmic problemsMedium

  This multi-part question evaluates algorithmic problem solving, data-structure selection, complexity analysis, API design, and the handling of trees, arrays, grids, circular structures, and dynamic updates.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Solve substring and top‑K pair problemsMedium

  This problem tests your ability to solve a sliding window string problem followed by a heap-based retrieval task for ranked items.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Sort the values of a quadratic function applied to a sorted array in linear timeMedium

  Given an integer array sorted in ascending order and the coefficients of a quadratic function, apply the function to every element and return the results in sorted order in linear time. Tests reasoning about the shape of a parabola, handling of positive, negative and zero leading coefficients, and duplicate values.

  LinkedInMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Sum Every Element of an N-Dimensional Array Readable Only by Index ListsHard

  Compute the sum of all elements of an array with any number of dimensions when the array exposes only its dimension sizes and a get method that reads one element from a full list of indices. It tests systematic enumeration of every index combination and exact handling of sums beyond the 32-bit range.

  LinkedInMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Test If Initial Video Uploads Are Shorter Than Later OnesMedium

  Evaluates hypothesis testing for whether users' first video uploads are shorter than later uploads. Strong answers define a clean data slice, paired user-level metrics, robust tests, and SQL-style implementation.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• Test a New Feature at Its Natural Product PlacementMedium

  Design a feature launch experiment that separates natural-placement availability, discoverability, promotional exposure, and downstream product value.

  LinkedInData ScientistAnalytics & ExperimentationOnsite