Meta Interview Questions

Meta 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

Design cluster-randomized test under network effects

A/B Test Design for a New Group Call Feature with Network Effects You are designing an experiment for a Group Call feature where social network effect...

Analytics & Experimentation
7
0
49 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Compute fraud probabilities with Bayes and Binomial

Fake-Account Detection with Binomial Sessions and Bayes Updating You are evaluating a rules-based detector for fake accounts on an online platform. Ea...

Statistics & Math
10
2
91 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design a feed ads A/B test with guardrails

Experiment Design: Insert One Extra Ad Every 8 Organic Posts in Main Feed Context You want to increase ad load by inserting one additional ad for ever...

Analytics & Experimentation
2
0
36 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Software Engineer

Design a YouTube/Netflix video platform

Design a video platform similar to YouTube/Netflix. Requirements: - Upload videos (for YouTube-like) and/or ingest catalog (for Netflix-like). - Trans...

System Design
3
0
64 people solved
Feb 25, 2026
Meta logo
Meta
Medium
Software Engineer

Design a chat messaging system

Design a chat system similar to WhatsApp/Messenger. Requirements: - 1:1 and group chats. - Send/receive messages in real time when online; store-and-f...

System Design
6
0
43 people solved
Feb 25, 2026
Meta logo
Meta
Medium
Software Engineer

Design a price drop tracking service

Design a price tracking service like CamelCamelCamel. Requirements: - Users track products and receive alerts when price drops below a threshold. - Pe...

System Design
6
0
54 people solved
Feb 25, 2026
Meta logo
Meta
Easy
Data Scientist Locked

Compute probability an account is fake

This question evaluates understanding of conditional probability and Bayesian reasoning, specifically interpreting base rates alongside true positive ...

Statistics & Math
28
1
448 people solved
Jan 17, 2026
Meta logo
Meta
Easy
Data Scientist

Handle conflict and urgent shifting priorities

Answer the following behavioral questions with concrete examples from your experience: 1. Describe a conflict you had with a partner or teammate. What...

Behavioral & Leadership
10
0
97 people solved
Jan 17, 2026
Meta logo
Meta
Medium
Data Scientist

Evaluating a 15 % reduction in post‑card height

Evaluating a 15 Percent Reduction in Post-card Height You own the feed UX for a social app. Designers propose shrinking each post card's height by 15 ...

Analytics & Experimentation
147
1
112 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Machine Learning Engineer

Demonstrate leadership and ensure data compliance

Behavioral & Leadership: Leading Through Reorg While Shipping ML + Ensuring Data Compliance Context You are the ML lead during a reorganization that r...

Behavioral & Leadership
7
0
61 people solved
Sep 6, 2025
Meta logo
Meta
Hard
Software Engineer

Design a real-time messenger

Question Design a real-time messaging system (a Messenger/WhatsApp-style product) that supports 1:1 and group chats. Walk through requirements, the AP...

System Design
8
0
62 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Software Engineer

Design an online coding platform

Design an online coding practice and interview platform (similar to well-known coding challenge sites). The platform should allow: - Users to sign up,...

System Design
4
0
44 people solved
Dec 8, 2025
Meta logo
Meta
Hard
Data Scientist

Design Machine Learning Model for Facebook Groups Post Ranking

Design Machine Learning Model for Facebook Groups Post Ranking ML System Design: Ranking Facebook Groups Posts in News Feed Scenario You are designing...

Machine Learning
4
0
51 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Software Engineer

Implement Multi-Level In-Memory Services

Implement the following independent multi-level in-memory service simulations. In an interview, you may receive one scenario and unlock the levels seq...

Coding & Algorithms
0
0
10 people solved
Apr 4, 2026
Meta logo
Meta
Easy
Analytics Engineer Locked

Detect fake accounts and measure their impact

This question evaluates competency in fraud detection, causal impact measurement, experimentation design, and operational analytics for product and ad...

Analytics & Experimentation
3
0
30 people solved
Feb 15, 2026
Meta logo
Meta
Medium
Data Scientist Locked

Evaluate a new-listing notification feature

This question evaluates product analytics, causal inference, experimentation design, metric definition, and business-impact estimation competencies fo...

Analytics & Experimentation
3
0
28 people solved
Feb 15, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Implement fast power and k-palindrome

This question evaluates numerical algorithm design and string algorithm skills, focusing on efficient exponentiation with negative exponents and deter...

Coding & Algorithms
5
0
44 people solved
Apr 1, 2026
Meta logo
Meta
Medium
Data Scientist

Evaluate new-product notification feature

A marketplace team is considering building a feature that notifies buyers when new products relevant to their interests are listed. How would you dete...

Analytics & Experimentation
4
0
33 people solved
Jan 5, 2026
Meta logo
Meta
Hard
Data Scientist

Design an experiment to evaluate a new ads algorithm

You are a Product Analytics/Data Science partner for an ads ranking/recommendation team. Facebook has shipped (or plans to ship) a new ad recommendati...

Analytics & Experimentation
3
0
43 people solved
Aug 21, 2025
Meta logo
Meta
Medium
Machine Learning Engineer Locked

Design concurrent expiring job registry

This question evaluates understanding of concurrent data structures, synchronization primitives, time-based expiration semantics, and efficient cleanu...

Software Engineering Fundamentals
2
0
29 people solved
Nov 28, 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.

Explore more Meta interview questions

Jump straight to Meta questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Meta interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Software Engineer
Meta interview
Read the guide
Editorial prep
Data Scientist
Meta interview
Read the guide