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 391–420 of 666 results

  • This question evaluates proficiency in string algorithms, substring search techniques, and algorithmic complexity analysis within the Coding & Algorithms domain.

    MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates array and tree algorithm skills, testing competency in identifying longest balanced subarrays in binary sequences and locating maximal strictly increasing downward paths in binary trees.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's ability to reason about interval overlap counting, manage range endpoints efficiently, and balance time-space trade-offs when identifying points that maximize coverage.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • Find k most frequent elements, discuss O(n) evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details 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 EngineerCoding & AlgorithmsTechnical Screen
  • A classic Meta software engineer onsite coding question: return the k-th largest element from an unsorted integer array, handling duplicates correctly. It tests order statistics and selection algorithms — sort, size-k min-heap (O(n log k)), and quickselect (O(n) average) — and the candidate's ability to beat a full sort.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates algorithmic problem-solving and data-structure knowledge, focusing on selection in a row-and-column-sorted matrix and the comparative use of min-heaps versus value-space binary search along with time/space complexity and edge-case analysis.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • This question tests the ability to perform multi-step data aggregation using SQL, specifically focusing on joining, filtering by date ranges, calculating distinct counts, and identifying bottom-tier performers across different time periods.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • This problem tests your ability to optimize string processing algorithms, specifically moving from a brute-force approach to an efficient expansion-based technique for identifying palindromic patterns.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • Find minimum and most frequent number efficiently evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details 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 EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's proficiency in array traversal, interval reasoning, handling edge cases including empty inputs and 32-bit integer boundary conditions, and enforcing time and space complexity constraints.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates algorithm design and analysis skills—particularly time–space tradeoffs, frequency counting techniques (sorting, hashing, and counting arrays), and handling streaming or memory-constrained inputs in the Coding & Algorithms domain.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Find power-of-two subarrays within sum range states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates proficiency with array algorithms and range-query reasoning, testing understanding of max/min behavior over contiguous subarrays and use of appropriate data-structure techniques; it belongs to the Coding & Algorithms domain.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency in string processing, handling character-uniqueness constraints, and algorithmic thinking about time and space efficiency.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's ability to design exploration and state-space search strategies for a robot operating in an unknown, partially blocked 2D grid using a constrained movement and sensing API, testing competency in traversal algorithms, state management, and handling limited observability.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • Given an integer array and a target, return the indices of the only pair of different elements whose values add up to the target, smaller index first. Arrays hold up to 10,000 values of up to one billion in magnitude, and the task is a common warm-up that tests clean, efficient array reasoning.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's mastery of finding the k-th largest element using selection algorithms and heap data structures, along with algorithmic time and space complexity analysis and techniques for adapting solutions to streaming inputs.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates data manipulation and metrics-definition skills, emphasizing SQL and Python proficiency for aggregating user activity and joining app metadata within analytics workflows.

    MetaData ScientistData Manipulation (SQL/Python)Onsite
  • This question evaluates understanding of streaming algorithms, frequency estimation, and data-structure-based top-K selection, emphasizing space/time and accuracy trade-offs when processing large or unbounded data.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates a candidate's understanding of permutation generation and duplicate handling within algorithm design, testing combinatorial reasoning, correctness proofs, and time/space complexity analysis.

    MetaMachine Learning EngineerCoding & AlgorithmsOnsite
  • A metrics question asking a product manager to set goals and success metrics for a scam-prevention product across Meta's apps. It tests goal prioritization, the choice and measurement of a north-star metric, leading indicators by scam type and surface, guardrails against over-enforcement, and measurement under adversarial behavior.

    MetaProduct ManagerAnalytics & ExperimentationOther
  • Practice a Google and Roomba acquisition strategy case with smart-home fit, user value, privacy governance, competitive landscape, financial modeling, regulatory risk, integration planning, alternatives, and a clear go or no-go recommendation.

    MetaProduct ManagerProduct / Decision MakingOnsite
  • Return a binary tree's node values grouped by vertical column from left to right, ordering each column top to bottom and breaking ties within a row by left-to-right position. The tree arrives as a level-order list, and the task tests coordinate tracking during traversal and precise tie handling.

    MetaSoftware EngineerCoding & AlgorithmsOnsite
  • This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Group a tree by vertical columns states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This problem tests your ability to navigate an unknown environment by maintaining internal state and implementing an efficient search algorithm to reach a target.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates interpersonal competencies such as conflict resolution, relationship management, communication, ownership, and accountability in scenarios involving missed delivery deadlines and team dynamics.

    MetaSoftware EngineerBehavioral & LeadershipOnsite
  • This question evaluates an Analytics Engineer's competencies in collaboration, inclusive communication, conflict resolution, constructive feedback delivery, and documentation practices within the Behavioral & Leadership domain.

    MetaAnalytics EngineerBehavioral & LeadershipOnsite
  • Handle feedback and priority conflicts 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
  • A Meta Data Scientist technical-screen behavioral question covering three leadership prompts: giving or receiving constructive feedback, pivoting when a project's direction or business conditions change, and resolving conflict with a teammate or stakeholder. The model answer gives a STAR rubric, strong vs. weak signals, and a practical template for each story.

    MetaData ScientistBehavioral & LeadershipTechnical Screen
  • This question evaluates string manipulation and numeric representation skills, specifically handling decimal-point addition with arbitrary precision and reasoning about palindrome/permutation properties.

    MetaSoftware EngineerCoding & AlgorithmsTechnical Screen

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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