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

Hiking App: Design, Metrics, and Go-to-Market

Hiking App: Design, Metrics, and Go-to-Market You are a Product Manager asked to design a consumer hiking mobile app from zero. Assume iOS and Android...

Product / Decision Making
16
0
67 people solved
Jul 4, 2025
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Meta
Medium
Product Manager

Core Behavioral Reflections

Core Behavioral Reflections for Product Managers Prepare concise, structured responses for four Product Manager behavioral prompts: professional failu...

Behavioral & Leadership
9
0
57 people solved
Jul 4, 2025
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Meta
Medium
Data Scientist Locked

Investigate Falling Brand-Ad Spend

This question evaluates competency in data analysis, anomaly detection, causal inference, and diagnostic reasoning related to advertising performance,...

Analytics & Experimentation
2
0
54 people solved
Mar 12, 2026
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Meta
Medium
Software Engineer AI Locked

Solve array, tree, and maze problems

This question evaluates algorithmic problem-solving across arrays, binary trees, and grid pathfinding, testing competencies in array traversal and vis...

Coding & Algorithms
5
0
53 people solved
Mar 12, 2026
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Meta
Medium
Software Engineer

Solve parsing, counting, ranges, and window problems

Solve the following four algorithm problems: 1) Expression evaluator with + and : Given a string s containing non-negative integers, '+' and '' operat...

Coding & Algorithms
2
0
27 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design a scalable banking system

System Design: Core Banking Platform Problem Design a banking system that supports: - Account creation - Balance inquiry - Deposit and withdrawal - At...

System Design
3
0
44 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Clone list with random pointers

Question You are given the head of a singly linked list where each node has two pointers: next and random. The random pointer may be null or point to ...

Coding & Algorithms
3
0
45 people solved
Sep 6, 2025
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Meta
Hard
Software EngineerSenior+

Design a content processing service

System Design: High-Throughput Content Processing Service Context Design a content processing and moderation service for a large-scale media app. Cont...

System Design
4
0
46 people solved
Sep 6, 2025
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Meta
Hard
Software EngineerSenior+

Design a secure ML data platform

System Design: Secure, Ethical, Multi‑Tenant ML Data and Inference Platform Context Design a cloud-based ML platform used by multiple internal product...

ML System Design
5
0
40 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Demonstrate communication and teamwork

Behavioral and Communication Interview (Software Engineer Onsite) Context: You are a software engineering candidate. Answer concisely using STAR (Situ...

Behavioral & Leadership
2
0
29 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Minimum Round-Trip Flight Cost

You are planning a round trip and want to spend as little as possible on flights. You are given two integer arrays of equal length n: - departure[i] —...

Coding & Algorithms
0
0
5 people solved
Mar 6, 2026
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Meta
Medium
Data Scientist Locked

How to evaluate new listing notifications?

This question evaluates a data scientist's competency in experimental design, causal inference, metric selection (primary, secondary, guardrail), and ...

Analytics & Experimentation
3
0
32 people solved
Mar 2, 2026
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Meta
Medium
Data Scientist Locked

Assess Group Video Chat Demand

This question evaluates a data scientist's product analytics competencies including causal inference, experiment and questionnaire design, proxy metri...

Analytics & Experimentation
3
0
30 people solved
Mar 1, 2026
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Meta
Hard
Machine Learning EngineerSenior+ AI Locked

Find Words Containing Other Words

This question evaluates string-processing skills, knowledge of substring matching techniques and supporting data structures, and the ability to analyz...

Coding & Algorithms
1
0
30 people solved
Mar 1, 2026
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Meta
Medium
Machine Learning Engineer Locked

Solve Merge Lists and Vertical Traversal

This question evaluates proficiency with merging sorted linked lists and performing vertical traversal of binary trees, focusing on algorithms, data s...

Coding & Algorithms
7
0
51 people solved
Feb 28, 2026
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Meta
Hard
Software Engineer

Design a product notifications system

System Design: Push Notifications and In‑App Messaging Platform Context Design a notifications platform for a large consumer mobile app with millions ...

System Design
2
0
44 people solved
Aug 14, 2025
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Meta
Medium
Data Scientist Locked

Measure fake account prevalence

This question evaluates a data scientist's competency in fraud measurement, statistical estimation, experimental design, and model evaluation for dete...

Analytics & Experimentation
4
0
42 people solved
Feb 22, 2026
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Meta
Medium
Data Scientist Locked

Evaluate an ads algorithm change

This question evaluates competency in experiment design, causal inference, metric selection, product analytics, and evaluation of ad-ranking systems, ...

Analytics & Experimentation
8
0
69 people solved
Feb 22, 2026
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Meta
Easy
Data ScientistSenior+

Describe something you did to be inclusive

Describe something you did to be inclusive Behavioral: Inclusiveness Tell me about a time you did something to make your team or workplace more inclus...

Behavioral & Leadership
3
0
32 people solved
Aug 5, 2025
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Meta
Medium
Data Engineer

Analyze private-account product metrics

Analyze private-account product metrics A social network is building (or refining) a private account feature: any user can set their account to privat...

Analytics & Experimentation
6
1
77 people solved
Aug 4, 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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