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
Product Manager

Program Execution Deep Dive

Program Execution Deep Dive Describe an end-to-end program or product you led as a Product Manager. The interviewer wants to understand how you captur...

Product / Decision Making
10
0
58 people solved
Jul 4, 2025
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Meta
Medium
Product Manager

Meta Pay: Metrics & Prioritization

Product Metrics and Prioritization Prompt: Meta Pay Assume you are the PM for Meta Pay, the consumer payments system used across Meta apps such as Mes...

Product / Decision Making
15
0
56 people solved
Jul 4, 2025
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Meta
Easy
Data Scientist

Compute maximum score using up to 3 categories

Problem A library runs a summer reading program. Each book a student reads earns a certain number of points, and each book belongs to a category. A st...

Coding & Algorithms
4
0
71 people solved
Dec 2, 2025
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Meta
Medium
Machine Learning Engineer Locked

Find longest palindromic substring

This question evaluates knowledge of string algorithms and pattern recognition, focusing on identifying palindromic substrings and reasoning about tim...

Coding & Algorithms
6
0
66 people solved
Nov 28, 2025
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Meta
Medium
Software Engineer Locked

Design an online chess game with undo and leaderboard

This question evaluates proficiency in real-time system design, state management, and concurrency control for multiplayer games, including consistency...

System Design
8
0
62 people solved
Nov 22, 2025
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Meta
Easy
Software Engineer

Design a time-versioned key-value store and find common free time

You are given two coding tasks. Task 1: Time-versioned key-value store Design an in-memory data structure that supports: - set(key, value, timestamp):...

Coding & Algorithms
4
0
32 people solved
Nov 12, 2025
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Meta
Medium
Software Engineer Locked

Implement several string, tree, and BFS problems

This multi-part problem evaluates proficiency with core data structures and algorithms: binary tree traversal with column-aligned output, string parsi...

Coding & Algorithms
6
0
49 people solved
Oct 30, 2025
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Meta
Medium
Software Engineer Locked

Design an Instagram-like auction platform

This question evaluates expertise in large-scale system design, including distributed systems, data modeling, real-time messaging, consistency models,...

System Design
1
0
23 people solved
Oct 30, 2025
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Meta
Medium
Software Engineer

Find shortest subarray with range ≥ k

Problem You are given an integer array nums of length n and an integer k. Define the range of a subarray as: \[ \max(\text{subarray}) - \min(\text{sub...

Coding & Algorithms
3
0
34 people solved
Oct 23, 2025
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Meta
Hard
Software Engineer Locked

Design an ad click tracking system

This question evaluates a candidate's ability to architect a high-throughput, reliable ad event tracking system, including event modeling, streaming a...

System Design
2
0
33 people solved
Oct 21, 2025
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Meta
Medium
Product Analyst Locked

How to evaluate emoji reactions?

This question evaluates product analytics and experimentation competencies for a Product Analyst role in the Analytics & Experimentation domain, focus...

Analytics & Experimentation
2
0
34 people solved
Oct 20, 2025
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Meta
Medium
Data Scientist

Write SQL to compare social-only vs game-only engagement

You are given two tables capturing Oculus app usage. Define an 'active day' as a UTC date on which a user generates at least one event. Consider only ...

Data Manipulation (SQL/Python)
40
1
336 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist Locked

Handle novelty and residual effects

This question evaluates a data scientist's competency in experiment design and causal inference for online metrics under temporal dynamics, specifical...

Analytics & Experimentation
2
0
26 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute cohort GMV and payer rate with edge cases

You are given the following schema (timestamps are UTC): users(user_id INT, country STRING, created_at TIMESTAMP) events(user_id INT, event_ts TIMESTA...

Data Manipulation (SQL/Python)
10
0
82 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute CTR overall and by campaign type

Write SQL to compute: (Q1) overall click-through rate (CTR = clicks/impressions) in the last week; (Q2) CTR by campaign_type in the last week. Assume ...

Data Manipulation (SQL/Python)
2
0
7 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL for retention, conversion, and churn

Assume today is 2025-09-01 (use the user's local day boundaries based on users.tz). Given the following schema and sample data, write SQL to: (a) Comp...

Data Manipulation (SQL/Python)
12
0
127 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist Locked

Design and analyze end-to-end A/B test

This question evaluates experimentation design and analysis competencies for A/B testing, including metric selection, statistical interpretation, inte...

Analytics & Experimentation
3
0
27 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist Locked

Define success metrics beyond time spent

This question evaluates a data scientist's competency in product analytics and experimentation, focusing on metrics design, cohort-based retention mea...

Analytics & Experimentation
2
0
23 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute binary-tree diameter via return-only DFS

Given the root of a binary tree, compute its diameter defined as the number of edges on the longest path between any two nodes. Implement a DFS that r...

Coding & Algorithms
6
0
45 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Write SQL to analyze Group Calls adoption

Write SQL (assume PostgreSQL) to analyze Group Calls adoption and cannibalization. Use this schema and sample data. Schema: - users(user_id INT PRIMAR...

Data Manipulation (SQL/Python)
0
0
4 people solved
Oct 13, 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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