Netflix interview questions

Showing 61–90 of 97 results

• Identify Longest Consecutive Incrementing Watch-Time SequenceMedium

  This question evaluates a candidate's ability to design and analyze efficient algorithms for detecting longest consecutive incrementing sequences in unsorted integer arrays, testing understanding of appropriate data structures and time/space complexity trade-offs.

  NetflixData ScientistCoding & AlgorithmsOnsite
• Implement Cache, Undo, and DFSMedium

  This problem tests your ability to combine a Least Recently Used (LRU) cache with a command-based undo system, applied to a hierarchical tree structure traversal.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Implement Caches, Undo, and TraversalMedium

  This question evaluates proficiency with data structures, API design, and system-level concerns by combining timed in-memory caching, command undo/redo semantics, and depth-first traversal of a tree; it falls under the Coding & Algorithms domain and is commonly asked to assess practical implementation skills, correctness under edge cases, and the ability to reason about time and space complexity. The level of abstraction spans practical application—designing clean APIs and implementing core logic—while also requiring conceptual understanding of complexity analysis and background cleanup or state-management patterns.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Implement Streaming Word CounterMedium

  This question evaluates streaming data processing, string tokenization, and frequency-aggregation competencies, focusing on maintaining accurate counts over incremental text inputs using associative data structures.

  NetflixMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement TTL Cache and Tree Balance ReportingMedium

  This question evaluates proficiency in designing efficient in-memory data structures and tree algorithms, specifically time-to-live cache behavior (expiration semantics and accurate unexpired counts) and per-node subtree-height computations for balance reporting, and it falls under the Coding & Algorithms domain.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Key-Value Cache with Per-Entry ExpirationEasy

  This problem tests the ability to design an in-memory storage system that combines fixed-capacity constraints with time-based invalidation logic.

  NetflixSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement a Versioned Key-Value StoreHard

  This question evaluates understanding of time-versioned data storage, related data-structure selection, handling of overwrite semantics at equal timestamps, and the ability to reason about correctness and complexity trade-offs.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Weighted Eviction CacheHard

  This problem tests your ability to design a cache that respects both memory capacity and item weights, requiring an efficient combination of a hash map and a doubly linked list.

  NetflixData EngineerCoding & AlgorithmsTechnical Screen
• Implement an in-memory key-value store with per-key TTL expiration and testsMedium

  A coding interview question that asks you to implement an in-memory key-value store whose entries expire after a per-key time-to-live, explain the design as you go, and test it. It tests expiry semantics, overwrite handling, reclaiming memory from keys that are never read again, complexity trade-offs and deterministic tests with a controllable clock.

  NetflixSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement longest increasing subarray with one deletionMedium

  This question evaluates proficiency in array algorithms and algorithmic optimization, focusing on handling boundary cases, reconstructing index ranges, and meeting strict O(n) time and O(1) extra-space constraints.

  NetflixData ScientistCoding & AlgorithmsOnsite
• Implement ordering and undo executorMedium

  This question evaluates graph algorithm skills (topological ordering and cycle detection) and software design competencies related to command interfaces, undo semantics, and shared-state management.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Implement phrase search and JSON pathHard

  This problem tests your ability to process unstructured text into a searchable index and navigate hierarchical data structures using path patterns.

  NetflixData EngineerCoding & AlgorithmsOnsite
• Implement weighted-eviction cacheMedium

  This question evaluates understanding and practical implementation of data structures and algorithms for a weighted cache, including weighted eviction policies, use of ordered maps, and time-space complexity analysis.

  NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
• L1 vs L2 Normalization: Definitions, Geometry and When to Use EachHard

  A machine learning fundamentals question asking how L1 normalization differs from L2 normalization of a vector. It tests precise definitions, what each normalized vector represents geometrically, when each is the right choice, and the ability to keep normalization distinct from L1 and L2 regularization.

  NetflixMachine Learning EngineerMachine LearningTechnical Screen
• ML Basics Check: Bayes' Rule, Forward-Pass Arithmetic, KNN and Transformer ConceptsEasy

  A set of basic machine learning checks: a Bayes' rule posterior with a low base rate, forward-pass and parameter-count arithmetic for small neural networks, and true-or-false concept checks on k-nearest neighbors and Transformer attention. It tests quick, accurate reasoning on core ML fundamentals.

  NetflixMachine Learning EngineerMachine LearningOnline Assessment
• Model Direct-Sold DSP OrdersMedium

  This Software Engineering Fundamentals question evaluates data and domain modeling skills for ad-technology order management, focusing on entities such as advertisers, agencies, orders, line items, targeting, creatives, delivery constraints, tracking events, reporting needs, and configuration versioning.

  NetflixSoftware EngineerSoftware Engineering FundamentalsOnsite
• Model ads demand data for reportingMedium

  This question tests the ability to design a robust data pipeline and schema for tracking ad performance metrics, focusing on handling event-level data, aggregations, and dimension consistency.

  NetflixSoftware EngineerSystem DesignOnsite
• Model data for an ads platformMedium

  This question evaluates proficiency in data modeling and database schema design for advertising systems, including entity-relationship modeling, privacy-aware user identifier handling, and delivery/tracking log representation.

  NetflixSoftware EngineerSystem DesignOnsite
• Movie Playlist With Live Score Updates and No Repeats per Loop, Plus Thread SafetyMedium

  Implement an in-memory movie playlist in which get returns the highest-scoring movie not yet served in the current loop, starting a new loop once every movie has been served, while scores can change at any time. A follow-up asks where to add locks when many threads call it concurrently.

  NetflixSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Multiply Two Sparse Integer Matrices Given as Dense GridsEasy

  Given two integer matrices stored as dense lists of rows in which most entries are zero, return their exact matrix product. It tests handling matrix dimensions correctly and exploiting sparsity to skip work on zero entries while still producing every entry of the result.

  NetflixMachine Learning EngineerCoding & AlgorithmsOnline Assessment
• Navigate conflicting signals and ambiguous expectationsMedium

  This question evaluates a Data Scientist's behavioral and leadership competencies — including adaptability to changing expectations, stakeholder management, prioritization between depth and breadth, decision-making principles, and post-outcome learning — and sits squarely in the Behavioral & Leadership domain.

  NetflixData ScientistBehavioral & LeadershipOnsite
• Plan Eviction and Cleanup for a Production CacheMedium

  This problem tests the design of an in-memory caching system, focusing on memory management, eviction strategies, and concurrency control to ensure high performance and stability.

  NetflixSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Reason About a Movie Billboard Rotation ComponentMedium

  This problem tests the ability to manage dynamic scheduling and priority updates using balanced binary search trees (ordered maps) while handling concurrency and system scalability.

  NetflixSoftware EngineerSoftware Engineering FundamentalsOnsite
• Recommend New Friends from a Mutual-Friendship Graph and Scale the ApproachMedium

  A problem-solving question about recommending new friends from a mutual friendship graph, worked mostly through discussion with a little code and pseudocode. It tests candidate generation and scoring with deterministic tie-breaks, cost in terms of node degree, handling highly connected users, and precomputing and refreshing suggestions at scale.

  NetflixSoftware EngineerSoftware Engineering FundamentalsOther
• Report Tree Levels and Balanced SubtreesMedium

  Tests traversing a binary tree stored in array (heap) layout to compute subtree heights and identify nodes rooted at height-balanced subtrees along with their tree levels.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Return the longest contiguous subarray with all distinct valuesMedium

  This problem tests your ability to use the sliding window pattern and a hash map to maintain a range of unique elements efficiently in O(n) time.

  NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
• Return valid task order from prerequisitesMedium

  This question evaluates proficiency in graph algorithms, notably topological sorting and cycle detection, as well as algorithmic complexity analysis and handling of dependency constraints.

  NetflixSoftware EngineerCoding & AlgorithmsOnsite
• Review a Feature Pipeline PR for Production Risks, Then Serve Features by FreshnessHard

  Review a feature pipeline pull request whose tests all pass and explain what could break once it runs in production, from time leakage and training-serving skew to data quality and scale. Then decide how the same features should be served when the model needs day-old, minute-fresh or request-time values.

  NetflixMachine Learning EngineerML System DesignTechnical Screen
• Session Token Cache With Per-Entry Expiry and a Test SuiteMedium

  Implement an in-memory cache whose entries expire individually, reclaim memory for keys that are never read again, and prove it with deterministic tests using an injected clock. Tests expiry semantics, stale-entry handling and test design.

  NetflixSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Show Behavioral Fit and ExperienceMedium

  Show Behavioral Fit and Experience evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  NetflixSoftware EngineerBehavioral & LeadershipHR Screen