Katkat

Company

Meta interview questions

666 practice questions in the style of Meta's interviews, covering Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Software Engineering Fundamentals, Statistics & Math, Behavioral & Leadership, Machine Learning, ML System Design, Product Design & Strategy, Product / Decision Making, System Design.

Questions666

54 easy472 medium140 hard

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Meta interview questions by the numbers

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Katkat has 666 Meta interview questions. The largest topics are Coding & Algorithms (32%), Analytics & Experimentation (17%) and Behavioral & Leadership (11%). By difficulty: 8% easy, 71% medium and 21% hard. The most common round is Onsite (54%). Frequent concepts include System Design, Causal Inference, Algorithms and Scalability.

Difficulty

  • Easy8% · 54
  • Medium71% · 472
  • Hard21% · 140

Interview rounds

  • Onsite54% · 358
  • Technical Screen40% · 268
  • Online Assessment3% · 23
  • HR Screen2% · 10
  • Other1% · 7

Roles

  • Software Engineer43% · 285
  • Data Scientist39% · 262
  • Machine Learning Engineer11% · 70
  • Data Engineer3% · 21
  • Product Manager2% · 13

Frequent concepts

  • System Design4% · 28
  • Causal Inference3% · 20
  • Algorithms3% · 19
  • Scalability3% · 19
  • Distributed Systems2% · 14
  • Metrics2% · 14

Showing 361–390 of 666 results

  • This question tests the ability to design a measurement framework for evaluating content-quality problems on a large user-generated-content platform. It assesses competency in metric selection, experimentation design, and causal reasoning within the Analytics & Experimentation domain — skills central to data science roles focused on platform health.

    MetaData ScientistAnalytics & ExperimentationTechnical Screen
  • This problem tests the ability to design an execution engine that manages task dependencies using a directed acyclic graph (DAG), respects concurrency limits, and handles task failures.

    MetaSoftware EngineerSoftware Engineering FundamentalsOnsite
  • This question evaluates a candidate's knowledge of database transactions, the ACID properties (Atomicity, Consistency, Isolation, Durability), and common isolation levels (Read Uncommitted, Read Committed, Repeatable Read, Serializable), testing competency in concurrency control and data consistency.

    MetaSoftware EngineerSoftware Engineering FundamentalsOnsite
  • Evaluates understanding of the Central Limit Theorem and confidence intervals in A/B testing. Strong answers explain sampling distributions, standard error, normal approximations for experiment metrics, caveats for dependence and heavy tails, and compute the 95% confidence interval as [2.902, 3.098].

    MetaData ScientistStatistics & MathOnsite
  • This question assesses a candidate's ability to navigate cross-functional friction, synthesize ambiguous project requirements into actionable tasks, and demonstrate maturity in making trade-offs under pressure.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • Tests an experienced engineer's ability to articulate business and technical impact, demonstrate end-to-end ownership, justify complex architectural trade-offs, and quantify measurable outcomes.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain Your Motivation for Career Transition and Role Interest states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    MetaData ScientistBehavioral & LeadershipTechnical Screen
  • This question evaluates your ability to take personal ownership of a significant technical or project failure, analyze root causes without deflecting blame, and implement lasting systemic improvements.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This question evaluates a product analyst's product thinking, analytical rigor, experiment design and execution, metric selection and trade-off analysis, and ability to quantify and communicate project impact while demonstrating leadership in end-to-end ownership.

    MetaProduct AnalystBehavioral & LeadershipTechnical Screen
  • This question evaluates project leadership, technical decision-making, trade-off analysis, cross-functional influence, and the ability to quantify impact with metrics.

    MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
  • Tests your ability to articulate an end-to-end production AI/ML system you designed, detailing the business justification, technical architecture, evaluation strategy, failure modes, and your individual contributions.

    MetaSoftware EngineerML System DesignTechnical Screen
  • Explain behavioral experiences and decisions 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.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This question tests your ability to articulate a complex technical contribution, demonstrate ownership through adversity, and communicate impact clearly to a non-expert interviewer.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Explain why IG Story usage exceeds Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    MetaData ScientistAnalytics & ExperimentationOnsite
  • This question evaluates a candidate's self-awareness, growth mindset, and ability to identify and develop specific professional skills or behaviors relevant to leadership, collaboration, and technical role effectiveness.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This problem tests your ability to adapt Breadth-First Search (BFS) to a state-space where the current location is augmented by a collection of keys, effectively transforming the grid into a larger, multi-layered graph.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates algorithmic problem-solving and software engineering skills, specifically graph traversal (BFS), state-space modeling for keys and doors, handling movement constraints, and debugging an existing codebase within the Coding & Algorithms domain.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates understanding of data structures and algorithms for stateful resource management, covering bookkeeping of per-user quotas, file metadata operations (copy, compress/decompress), string filtering and sorting, and efficient selection and deletion of largest elements.

    MetaSoftware EngineerCoding & AlgorithmsOnline Assessment
  • This question tests your ability to apply Bayes' Theorem in a low-prevalence classification scenario and your ability to engineer robust signals for detecting anomalous user behavior.

    MetaData ScientistStatistics & MathOnsite
  • This question evaluates grid-based graph traversal and algorithmic problem-solving skills, including concepts such as multi-source breadth-first search, handling sentinel values, and performing in-place updates.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to clean noisy labeling datasets and design metrics to detect annotator bias or performance degradation.

    MetaMachine Learning EngineerData Manipulation (SQL/Python)Onsite
  • A binary tree coding problem: given a tree with distinct values, a target node and a distance k, return every node exactly k edges away, including nodes reached by moving up through ancestors. It tests traversal in both directions of a tree, level-order input parsing and careful handling of visited nodes.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • Find the index of any strict local minimum in a nonempty array whose adjacent values differ, using an iterative logarithmic-time algorithm. The task tests search invariants, boundary-as-infinity semantics, singleton and monotone arrays, and correctness when several indices are valid.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This pair of problems evaluates array manipulation and algorithmic problem-solving skills, specifically order-statistics for selecting the k-th largest element and maximizing consecutive sequences under constrained transformations for the vacation streak problem.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates algorithmic problem-solving and data-structure competencies, focusing on selection algorithms and time/space complexity reasoning for the k-th largest element and tree traversal, lowest common ancestor discovery with attention to BST properties, parent pointers, and external-memory constraints.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to use bitmasking and backtracking with pruning to solve a variation of the maximum set packing problem under character constraints.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • Design a high-scale gaming leaderboard service capable of real-time score updates, deterministic tie-breaking, friend-filtered leaderboards, and efficient "nearby competitor" queries under extreme write loads.

    MetaSoftware EngineerSystem DesignOnsite
  • This problem tests your ability to efficiently manage and query prefix-based string sets using a Trie (prefix tree) data structure.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to efficiently identify all words in a list that contain at least one other word from the same list as a substring, utilizing optimized string processing or data structures like Tries.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • This problem tests the ability to adapt binary search to identify a local minimum in an unsorted array, focusing on handling boundary conditions and non-strict inequality logic.

    MetaSoftware EngineerCoding & AlgorithmsOnsite

Meta interview experiences

All 83 →
  • Full Onsite Loop Plus My AI Coding Interview PlaybookMeta · Software Engineer · Onsite · Sep 2026

    Most of the questions were the classic, well-worn ones you'd already find on the forum. Phone Leetcode easy + mid Onsite 2 AI coding rounds + 2 AI system design rounds + 1 BQ AI coding 01: Friend system: Implement a friend recommendation algorithm Given data: a list of users, a list of friendships (user-user pairs) Design a strategy to recommend new friends to a user (e.g., search for friends of…

  • Leadership and Fullstack Phone Screen, Rejected Over 'Not Enough Impact'Meta · Data Engineer · Senior+ · Technical Screen · Sep 2026
    Rejected

    From the recruiter to the interviewers, the whole thing gave off an absurd vibe. I was applying for a senior position, and the job description didn't even list a level at the time. After I applied, the recruiter had me submit my availability directly — no call at all. Once it was scheduled, I was told it would be one Leadership round and one Fullstack round. I thought that was a little strange at…

  • Offer After 1.5 Years Out of Work and 600+ Hours of PrepMeta · Software Engineer · Onsite · Sep 2026
    Offer

    I had been out of work for a year and a half. Once I got properly serious about the search, I fired off about 100 applications and basically got silence. A few startups got in touch, but nothing picked up speed. Then someone referred me at Meta and a recruiter asked to set up a call. One choice early on, I believe, defined everything that followed. I turned the startup conversations down and comm…

  • Explaining an AI System and Solving a Maze with AIMeta · Software Engineer · Technical Screen · Aug 2026

    This will most likely become a rejection story. At the beginning, I was asked to introduce an AI system I had designed and explain the problem it solved. I stumbled through the explanation, and the interviewer did not seem very satisfied. I probably did not make the project sound impressive enough. Next came AI coding. This really felt like a company going all in on AI, because everything involve…

  • Phone Screen Plus a Four-Round Virtual OnsiteMeta · Software Engineer · Onsite · Aug 2026

    Meta phone screen + virtual onsite writeup, with the questions and my thought process included. Since I signed an NDA, I'm coding the problem names as homophones — sorry about that. Phone Screen 45 minutes, two questions. The first five minutes were self-intro, and pretty much the rest of the time was spent coding. Question 1, coded 1091: a 2D grid, get from the top-left corner to the bottom-righ…

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