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
Easy
Data Scientist

Calculate Expected Day for First Selection in Sampling

Expected Day of First Selection in Daily Sampling There are 1,000 people. Each day, 10 distinct names are selected uniformly at random. Day counting s...

Statistics & Math
27
0
67 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist Locked

Determine Probability of Shared Videos in Recommendations

Meta statistics and product prompt on video recommendation overlap, covering combinations, probability of shared videos, expected intersection size, s...

Statistics & Math
58
0
113 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist Locked

Fake Accounts [AE]

Evaluates probability, classification metrics, and feature engineering for fake-account detection. Strong answers apply Bayes' rule with rate-weighted...

Statistics & Math
216
6
492 people solved
Jul 12, 2025
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Meta
Medium
Software Engineer

Find shortest path and compare BFS vs DFS

Given an unweighted graph (directed or undirected), explain how to find the shortest path between two nodes without writing code. Describe the algorit...

Coding & Algorithms
5
0
50 people solved
Sep 6, 2025
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Meta
Hard
Data Engineer

Design tables for event-driven metrics

Design a Relational Schema for Consumer-App Event Analytics Context and Assumptions You are designing the event store for a high-volume consumer app. ...

System Design
7
0
73 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design input validation and error handling

Design Task: API, Validation, and Error Handling for a Top-K Frequency Service Context Design a production-grade service that, given an array of eleme...

System Design
4
0
49 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design a bank system with transfers

Single-Process BankSystem: APIs, Data Model, Algorithms, and Edge Cases Context and Assumptions You are asked to design and implement a single-process...

System Design
4
0
64 people solved
Sep 6, 2025
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Meta
Medium
Data Engineer

Design a scalable dimensional model

Design a Dimensional Model for Transactional Analytics (Concrete Example Included) You are building a star-schema in a cloud data warehouse for near r...

System Design
13
0
91 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Explain a recent project and measured impact

Behavioral & Leadership: High-Impact Project Deep Dive You are interviewing for a software engineering role. Provide a concise, metrics-driven walkthr...

Behavioral & Leadership
4
0
49 people solved
Sep 6, 2025
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Meta
Medium
Software Engineer

Derive max distinct frequencies for n items

Maximum Number of Distinct Frequency Counts in an Array Context You are given an array of length n ≥ 1 whose elements are arbitrary integers (values m...

Statistics & Math
9
0
74 people solved
Sep 6, 2025
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Meta
Hard
Software Engineer

Design an online auction platform

System Design: Real-Time English Auction Platform Goal Design an online auction platform that supports English-style auctions with: - Reserve price - ...

System Design
10
0
80 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
Site Reliability Engineer Locked

Implement a Streaming VMStat Alert

This question evaluates streaming data processing skills, sliding-window counting algorithms, and efficient online state management for metric monitor...

Coding & Algorithms
5
0
73 people solved
Apr 6, 2026
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Meta
Medium
Machine Learning Engineer Locked

Design a newsfeed dislike model

This question evaluates a candidate's ability to design and operationalize a machine learning model that predicts the probability of a user disliking ...

ML System Design
8
0
67 people solved
Sep 4, 2025
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Meta
Medium
Software Engineer Locked

Solve delimiter and CSV tasks

This question evaluates skills in string parsing and bracket balancing, CSV/file parsing and joining, computation of derived values, and sorting, emph...

Coding & Algorithms
6
0
52 people solved
Apr 5, 2026
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Meta
Medium
Data Scientist

Count unconnected posts and reactions

You are analyzing a newly launched feed feature intended to improve engagement by showing more unconnected content. Assume the following tables: - pos...

Data Manipulation (SQL/Python)
21
2
200 people solved
Apr 5, 2026
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Meta
Medium
Product Manager

Dog-Walking Marketplace & Architecture

Design a Dog-Walking Marketplace App Design a two-sided marketplace that connects dog owners who need walks with vetted dog walkers who provide them. ...

Product / Decision Making
4
0
42 people solved
Jul 4, 2025
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Meta
Medium
Software Engineer

Implement tree column grouping and minimal parentheses fixes

Part A — Binary tree column grouping: Given the root of a binary tree, group node values by their vertical columns from leftmost to rightmost using x-...

Coding & Algorithms
3
0
32 people solved
Aug 13, 2025
Meta logo
Meta
Hard
Data Scientist

How would you evaluate upranking Shop ads?

Meta is considering upranking ads that send users to an in-app Shop experience (for example, Facebook/Instagram Shops) relative to ads that send users...

Analytics & Experimentation
3
0
42 people solved
Oct 16, 2025
Meta logo
Meta
Medium
Software Engineer

Solve array merge and tree flattening problems

Solve array merge and tree flattening problems 1) Given two sorted arrays, merge them into a single sorted array. Provide both an in-place approach (w...

Coding & Algorithms
1
0
33 people solved
Aug 9, 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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