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
Meta logo
Meta
Hard
Data Scientist Locked

Diagnose drop and assess metric change impact

This question evaluates a data scientist's competency in diagnostic analytics, instrumentation validation, causal attribution, experimentation design,...

Analytics & Experimentation
1
0
23 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Design and validate ad model launch

You are on the Ads team and just trained a new ad recommendation model meant to replace the current model in production. Design a rigorous plan to dec...

Analytics & Experimentation
2
0
33 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate revenue of organic shopping tab

Estimate Monthly Revenue for a New Shopping Tab (Organic Only) Context You are evaluating the potential monthly revenue impact of launching a new Shop...

Analytics & Experimentation
4
0
33 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Validate needs and benchmark competitor adoption

Research Plan: Validate User Needs and Benchmark Competitors' Adoption of Group Calling You are designing a research plan for a consumer communication...

Analytics & Experimentation
2
0
29 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Choose group-call size cap via experiment

Decide the Maximum Participants per Group Call: Experiment Plan Context: You need to choose a default cap for group calls (maximum concurrent particip...

Analytics & Experimentation
2
0
36 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Design experiment for Group Calls with interference

Design an Experiment for Group Calls in a 1:1 Calling App (with Network Interference) You are adding a Group Calls feature to an existing 1:1 calling ...

Analytics & Experimentation
1
0
25 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Justify EU B2B Chat Product Strategy

Executive Brief Task: EU B2C Customer-Service Chat (Subscription) Context You are asked to prepare a one-page executive brief recommending whether to ...

Behavioral & Leadership
4
0
47 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Compute posterior for accurate-but-rare classifier

Bayes' Theorem: Interpreting Screening Model Predictions Context You are evaluating a binary screening model that flags "bad" users in a population. T...

Statistics & Math
4
0
38 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist Locked

Compute and correct correlation significance inflation

This question evaluates statistical inference for correlations, multiple testing control (false discovery rate), power and sample-size calculations, a...

Statistics & Math
2
0
37 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Design experiment for unconnected content in feed

Analytics & Experimentation Case: Socialness of Friends vs Unconnected Content Context You work on a personalized feed that shows posts from friends a...

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

Justify building a new feature with evidence

Case Prompt: 10-Minute Go/No-Go Recommendation for a New Feature You are the data science lead supporting a large-scale consumer messaging product. Yo...

Analytics & Experimentation
3
0
46 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design and justify unread-accounts pinning experiment

Experiment Design: Pin Unread Accounts at Top of Account Switcher Context You propose a feature for users who own multiple accounts (same person_id): ...

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

Design metrics and geo A/B for new feature

Marketplace Experiment: Verified Seller Badges Context: You are evaluating a new Marketplace feature, Verified Seller Badges, designed to improve buye...

Analytics & Experimentation
2
0
25 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Manage promotions and project portfolio tradeoffs

Context You manage a 10-person Data Science team operating across multiple locations and time zones. Three senior individual contributors (ICs) are ac...

Behavioral & Leadership
5
0
44 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design an A/B test for WFH filter

A/B Test Design: Optional "Work From Home" Filter on Search Page You are designing an online controlled experiment for a marketplace search page that ...

Analytics & Experimentation
1
0
33 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate emoji reactions launch

A messaging app plans to introduce an emoji reaction feature: users can long-press a message for 5 seconds and attach an emoji instead of sending a te...

Analytics & Experimentation
2
0
27 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Software Engineer

Design user top-10 songs service

Design user top-10 songs service Design a Service to Compute Each User's Top 10 Songs Context You are designing a backend service for a large-scale mu...

System Design
2
0
31 people solved
Aug 7, 2025
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Meta
Hard
Software Engineer

Design an online auction system

Design an online auction system System Design: Real-Time Online Auction Platform You are asked to design an online auction system that supports real-t...

System Design
6
0
54 people solved
Aug 7, 2025
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Meta
Medium
Software Engineer Locked

Design a game leaderboard service

This question evaluates a candidate's ability to design scalable, low-latency ranking services, testing competencies in distributed systems, data mode...

System Design
1
0
26 people solved
Oct 12, 2025
Meta logo
Meta
Easy
Data ScientistSenior+

Explain why IG Story usage exceeds Facebook

Explain why IG Story usage exceeds Facebook Product analytics case: Instagram vs Facebook Stories You work on Stories across two apps: Instagram (IG) ...

Analytics & Experimentation
6
0
61 people solved
Aug 5, 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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