Meta Interview Questions

Meta System Design Interview Questions

Practice 1,159 real Meta interview questions for 2026. Covers top categories — Coding & Algorithms, Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, and System Design — across Software Engineer, Data Scientist, Machine Learning Engineer, Data Engineer, and Product Manager roles. Real questions from actual interviews with detailed solutions. Expect a software-engineering-heavy loop: timed algorithmic coding (trees, arrays, graph/maze problems, delimiter/CSV parsing), system-design prompts like leaderboards, flight search and online-judge architectures, and an increasingly common AI-assisted coding round that mirrors real workflows. Data Scientist rounds emphasize product analytics and experimentation—designing tests, diagnosing spend drops and bots, evaluating unconnected content, and writing SQL for multi-account, seller, and vehicle metrics. Machine Learning Engineer questions skew toward recommender and ranking work (place and friend recommendation, sparse-matrix ops, linear-regression derivations, newsfeed dislike models). Data Engineers focus on data modeling, ETL, capacity calculations, reservations/utilization queries, and production SQL/Python tasks. For interview preparation, prioritize timed coding practice, system-design templates, rigorous SQL drills (joins/CTEs/aggregation), clear A/B-testing frameworks, and concise STAR behavioral stories tied to measurable impact.

1.2k Questions 1 Company07.06.2026
Showing 20 results
Role
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Meta
Medium
Data Engineer

Write SQL for active follow connections

Table: follow_events(requester_id INT, target_id INT, event STRING CHECK (event IN ('request_follow','follow_success','follow_reject','unfollow')), ev...

Data Manipulation (SQL/Python)
2
2
29 people solved
Aug 1, 2025
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Meta
Medium
Machine Learning Engineer

Compute sliding-window medians

Given an array nums and an integer k, compute the median for each contiguous subarray (window) of length k and return the sequence of medians in order...

Coding & Algorithms
2
0
33 people solved
Jul 31, 2025
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Meta
Medium
Software Engineer

Solve diverse LeetCode algorithm problems

Question LeetCode 695. Max Area of Island LeetCode 380. Insert Delete GetRandom O( 1) LeetCode 19. Remove Nth Node From End of List LeetCode 1004. Max...

Coding & Algorithms
3
0
14 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Design Ticket Booking Auto Release

Design Ticket Booking Auto Release System Design: Auto-expiring Ticket Reservations Problem Design a ticket-booking system where a reserved ticket aut...

System Design
11
0
38 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Solve island connectivity and string multiplication

Question Given N distinct islands (e.g., a, b, c…) and a list of existing undirected paths between them, compute the minimum number of additional path...

Coding & Algorithms
11
0
51 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Design weighted random index picker

Design and implement a data structure that, given an array of positive integer weights w[0..n−1], supports pick() that returns index i with probabilit...

Coding & Algorithms
5
0
46 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Describe a recent project you led

Describe a recent project you led Behavioral Prompt: Project Leadership Walkthrough (Software Engineer) Provide a concise, end-to-end walkthrough of a...

Behavioral & Leadership
2
0
31 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Implement string sum and weighted city picker

Implement string sum and weighted city picker Part A — Add two numbers represented as strings: Given two non‑negative integers provided as decimal str...

Coding & Algorithms
3
0
36 people solved
Jul 17, 2025
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Meta
Hard
Software Engineer

Design a scalable key-value configuration service

Design a scalable key-value configuration service Design a Globally Distributed Key–Value Configuration Service Background You are asked to design a g...

System Design
4
0
48 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Validate word abbreviation and reconcile two abbreviations

Validate word abbreviation and reconcile two abbreviations Implement a function isValidAbbreviation(word: string, abbr: string) that returns true if a...

Coding & Algorithms
3
0
35 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Copy linked list with random pointers efficiently

Copy linked list with random pointers efficiently Given the head of a singly linked list where each node has next and random pointers, create a deep c...

Coding & Algorithms
1
0
31 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Solve tree traversal and two-pointer problems

Solve tree traversal and two-pointer problems Implement the following independent problems: 1) Vertical columns: Given a binary tree, return lists of ...

Coding & Algorithms
3
0
40 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Describe conflict resolution, prioritization, and collaboration

Describe conflict resolution, prioritization, and collaboration Behavioral Prompt: Conflict Resolution and Leadership (Software Engineer, Onsite) Inst...

Behavioral & Leadership
3
0
52 people solved
Jul 15, 2025
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Meta
Medium
Data Scientist

Resolve Team Conflicts and Exceed Job Expectations Successfully

Resolve Team Conflicts and Exceed Job Expectations Successfully This behavioral prompt explores initiative beyond formal responsibilities and the abil...

Behavioral & Leadership
27
0
40 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Convince Product Manager to Launch 'Show Similar Products' Button

Convince a PM to Test a "Show Similar Products" Button Instagram is considering adding a "Show similar products" button on product-tagged content to b...

Analytics & Experimentation
5
0
44 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Probability Distributions for Modeling Ad Clicks

Identify Probability Distributions for Modeling Ad Clicks You are interviewing for a data scientist role on an ads team. The interviewer asks you to d...

Statistics & Math
72
0
142 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Design Experiment to Test New Hashtag Recommender Algorithm

Experiment Design: Testing a New Hashtag Recommender A social app shows hashtag recommendations to users while they compose posts. A new algorithm is ...

Analytics & Experimentation
13
0
32 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Unique Callers and French Customer Call Percentage

video_calls +---------+-----------+--------------+---------------------+---------------+ | call_id | caller_id | recipient_id | start_ts | ...

Data Manipulation (SQL/Python)
86
0
276 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze Distribution of Daily Page Shares Per User

Engagement Distributions and Cohort Dynamics You are analyzing per-user, per-day engagement. Assume the panel includes all users, inactive days count ...

Statistics & Math
87
2
127 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Probabilities for Mixed Reviewer Types

Probabilities for Mixed Reviewer Types Two types of reviewers exist in a marketplace: - Lazy reviewers are 20% of reviewers and always give good revie...

Statistics & Math
84
0
219 people solved
Jul 12, 2025

Frequently Asked Questions

How difficult are Meta interview questions?
Meta interview questions span a wide difficulty range because they must screen candidates from entry to senior levels across many functions. Expect coding rounds to map to medium-to-hard algorithmic problems that appear in top 100 problem lists for software engineers, and expect data roles to face challenging SQL, experiment diagnosis, and product-analytics problems that require clean metric definitions. Machine learning roles emphasize recommendation and ranking tradeoffs and model complexity, while data engineers encounter large-scale ETL and modeling puzzles. Difficulty scales with level: entry hires see clearer, bounded problems; senior hires face ambiguous tradeoffs and system-wide thinking.
What is Meta's interview process and where do these questions appear?
Meta typically runs a multi-stage process: recruiter screen, one or two technical screens or an online assessment, a full loop of onsite-style interviews, then debrief, committee review, and offer. The full loop mixes coding, system or product design, role-specific technical rounds, and behavioral interviews. Software-engineer candidates spend most time on coding and design; data scientists focus on SQL, experimentation, and product analytics; machine-learning engineers see modeling and recommendation design; data engineers handle SQL, data modeling, and pipeline questions; PMs get product-design and analytics probes. In 2025–2026 some teams pilot AI-enabled coding rounds.
How should I structure a preparation timeline for a Meta interview?
A focused six-week plan works well: weeks one and two cover fundamentals—data structures, algorithms, SQL basics, and experiment design; weeks three and four emphasize timed problem practice, mock phone screens, and role-specific cases (A/B diagnosis for data scientists, model design for MLEs, ETL modeling for data engineers); week five concentrates on system or product design and behavioral storytelling; week six is for full mock loops, timing, and refining communication. Practice with realistic tools, simulate loop pacing, and schedule a debrief after each mock to iterate on clarity, edge-case handling, and time management.
Which technical subtopics are most commonly tested for each role at Meta?
For Data Scientist interviews the recurring technical themes are product-metric definition, diagnosing experiment and spend drops, counting multi-account interactions, SQL for multi-entity metrics, and ranking or recommendation evaluation such as shop ad ranking. Software-engineer questions frequently focus on timestamped state and versioned systems, leaderboards and ranking, maze/graph traversal and tree/array transforms, delimiter and CSV parsing, and scalable search or flight-search style designs. Machine-learning engineers see place and friend recommendation design, sparse-matrix operations, ranking/loss choices, and feed dislike or personalization models. Data engineers repeatedly face entity modeling for feed and booking data, SQL analytics for utilization and reservations, and capacity-aware aggregation challenges.
What standout tips and common pitfalls should I watch for in Meta interviews?
Start interviews by clarifying requirements and expected outputs, then propose measurable success metrics; this prevents misaligned solutions. For coding, think aloud, handle edge cases, state complexity up front, and write a couple of quick tests. In design rounds quantify load, storage, and tradeoffs rather than vague features. Data roles must define metrics, guardrails, and experiment assumptions before jumping to analysis; common pitfalls are ambiguous metric definitions, peeking at tests, and ignoring instrumentation limits. For AI-assisted coding rounds, use the assistant to accelerate boilerplate but validate logic and corner cases yourself. Finish each answer with a concise summary of impact and tradeoffs.

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