LinkedIn interview questions

Showing 91–120 of 154 results

• Find the K Values Closest to a Target in a Binary Search TreeMedium

  Given a binary search tree described by its preorder traversal, a real-valued target and an integer k, return the k node values closest to the target in ascending order, breaking distance ties toward the smaller value. Tests binary search tree traversal, reasoning over ordered data and precise tie handling.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Find the Maximum Depth of a Binary TreeMedium

  Find binary-tree depth from a portable child-index representation, including empty trees and highly unbalanced root-to-leaf paths.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Find the Nearest Supply Location in a Road NetworkMedium

  This problem tests your ability to model a road network as a graph and compute the shortest distance from a source node to any target node in a set using a Breadth-First Search (BFS) approach.

  LinkedInDevOps EngineerCoding & AlgorithmsOnsite
• Find the Node Where Two Singly Linked Lists MergeMedium

  Two singly linked lists stored in one successor array may merge at some node and share every node after it. Given both heads, return the first shared node, or -1 if the lists never meet. It tests linked-list traversal and careful handling of cases such as one list starting inside the other.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Find the k-th largest element in an arrayMedium

  This question evaluates proficiency with selection algorithms, array manipulation, and analysis of time and space complexity, reflecting competency in order-statistics and basic data structures.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Generate All Distinct Permutations of a List with Duplicate ValuesMedium

  Given a list of up to eight integers that may contain duplicates, return every distinct permutation exactly once, sorted in lexicographic order. It tests systematic enumeration, avoiding duplicate results caused by equal values, and producing a deterministic output order.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Generate uniform 0–6 from biased coinMedium

  This question evaluates understanding of randomized algorithms, probability theory, and techniques for extracting uniform randomness from a biased source.

  LinkedInMachine Learning EngineerCoding & AlgorithmsOnsite
• Handle Issues and Onboard TeammatesEasy

  This question evaluates leadership, cross-functional communication, incident management, and knowledge-transfer competencies by probing how a candidate resolves stakeholder disagreements and explains team-owned systems.

  LinkedInSoftware EngineerBehavioral & LeadershipOnsite
• Handle imbalance, validate samples, and avoid overfittingEasy

  This question tests your ability to design a robust machine learning pipeline for a highly imbalanced classification task, focusing on sampling strategies, metric selection, and regularization techniques to prevent overfitting.

  LinkedInData ScientistMachine LearningTechnical Screen
• How do you sample uniformly from an infinite stream?Easy

  This question tests the implementation of Reservoir Sampling to maintain a uniform random sample of size k from a stream of unknown or infinite length.

  LinkedInData ScientistCoding & AlgorithmsTechnical Screen
• How do you win project buy-in?Medium

  This question evaluates leadership, stakeholder management, and team resiliency competencies by probing experience in securing project buy-in and in preventing single-point-of-failure through knowledge sharing, documentation, and ownership distribution.

  LinkedInSoftware EngineerBehavioral & LeadershipTechnical Screen
• How to analyze Simpson's paradoxMedium

  This question evaluates competency in causal reasoning, statistical inference, experimental design, and detection of Simpson's paradox within the Analytics & Experimentation domain.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• How to diagnose traffic and measure relevance?Hard

  This problem tests your ability to diagnose an unexplained drop in engagement metrics by decomposing a complex user funnel, isolating root causes using multidimensional data, and designing a robust validation framework.

  LinkedInData ScientistAnalytics & ExperimentationTechnical Screen
• How would you lead a team to improve quality?Easy

  This question evaluates leadership and technical execution competencies for a data scientist in a tech lead role, including work planning, delegation, accountability, stakeholder communication, root-cause diagnosis of product quality issues, metric definition, and delivering projects with measurable impact.

  LinkedInData ScientistBehavioral & LeadershipTechnical Screen
• Implement Fast Power with Negative ExponentsMedium

  This problem tests your ability to implement the exponentiation by squaring algorithm while correctly handling edge cases like negative exponents and integer overflow.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Implement K-Means and Explain ConvergenceMedium

  This problem tests the implementation of the K-means clustering algorithm from scratch, requiring an understanding of the iterative refinement process, distance metrics, and the underlying mathematical objective.

  LinkedInMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement a Text-Embedding Recommender Training PipelineHard

  Tests the ability to structure an end-to-end, leakage-resistant training pipeline for a text-embedding recommender, covering profile flattening, embedding caching, temporal splits, binary classification, and artifact serialization.

  LinkedInMachine Learning EngineerMachine LearningTechnical Screen
• Implement a stack supporting max and popMaxMedium

  This question evaluates understanding of data structures and algorithmic complexity, focusing on designing a stack variant that supports efficient retrieval and removal of the current maximum while preserving stack behavior.

  LinkedInData EngineerCoding & AlgorithmsTechnical Screen
• Implement alert queries and spike detectionMedium

  This problem tests your ability to process a stream of events with time-based constraints, requiring an efficient sliding-window approach and the use of monotonic data structures for spike detection.

  LinkedInMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement an LRU cache with follow-upsMedium

  This question evaluates understanding of core data structures and algorithms for designing an efficient LRU cache as well as knowledge of concurrency and synchronization for thread-safe access.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Implement fast sampling for weighted k-sided dieMedium

  This question evaluates understanding of probabilistic algorithm design, numerical stability, and efficient data-structure implementation for constant-time sampling from categorical distributions.

  LinkedInData ScientistCoding & AlgorithmsTechnical Screen
• Implement map and render to DOMMedium

  This question evaluates a candidate's skills in vanilla JavaScript utility implementation and DOM rendering, covering array and plain-object iteration semantics, higher-order functions, input validation, error handling, and safe text rendering for browser environments.

  LinkedInSoftware EngineerSoftware Engineering FundamentalsHR Screen
• Implement stream random sampling in PythonMedium

  This question evaluates understanding of streaming algorithms, uniform random sampling, and probabilistic guarantees for maintaining a fixed-size sample from an unbounded data stream (reservoir-sampling concepts).

  LinkedInData ScientistCoding & AlgorithmsTechnical Screen
• Improve Profile Completion RateMedium

  Evaluates analytics and experimentation strategy for improving LinkedIn profile completion. Strong answers define value-weighted completion metrics, segment gaps, prioritize interventions, and test impact with guardrails.

  LinkedInData ScientistAnalytics & ExperimentationOnsite
• Introduce yourself and discuss challengesMedium

  This question evaluates communication, leadership, self-awareness, and the ability to concisely articulate technical impact, trade-offs, and measurable outcomes.

  LinkedInSoftware EngineerBehavioral & LeadershipHR Screen
• Largest Sum of a Non-Empty Contiguous SubarrayMedium

  Given an integer array, return the largest sum of any non-empty contiguous subarray, including arrays in which every number is negative. This classic phone-screen problem tests efficient array processing and careful handling of edge cases such as all-negative input.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Lexicographically Smallest Topological Order of a Dependency GraphMedium

  Given n labeled nodes and directed precedence edges, return an ordering that respects every edge, choosing the lexicographically smallest valid ordering, or an empty list when a cycle makes ordering impossible. It tests topological sorting, cycle detection and deterministic tie-breaking.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Longest Run of Ones After One FlipMedium

  This problem tests your ability to manage a sliding window with a flexible constraint, specifically counting zeros to determine the maximum length of a sequence after a single modification.

  LinkedInSoftware EngineerCoding & AlgorithmsTechnical Screen
• Maximum Product of a Contiguous Subarray With Negatives and ZerosMedium

  Given an integer array that may contain negative numbers and zeros, return the largest product of any non-empty contiguous subarray. The problem tests careful reasoning about sign changes, zeros and single-element subarrays under tight input-size limits.

  LinkedInSoftware EngineerCoding & AlgorithmsOnsite
• Measure Causal Impact of Self-Selected App RedesignHard

  LinkedIn causal inference prompt on measuring a self-selected app redesign, covering potential outcomes, ATT, propensity matching or weighting, staggered difference-in-differences, event studies, covariates, balance, and pre-trend validation.

  LinkedInData ScientistStatistics & MathOnsite