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
Medium
Software Engineer Locked

Add two binary strings

This question evaluates proficiency with binary arithmetic and string manipulation, focusing on carry propagation and handling of very long inputs wit...

Coding & Algorithms
3
0
33 people solved
Jan 5, 2026
Meta logo
Meta
Hard
Product Manager

Prioritize voice-to-text or assistant?

For the same accessibility-focused VR product, assume the team can ship only one feature first because engineering capacity is limited: voice-to-text ...

Product / Decision Making
4
0
37 people solved
Apr 10, 2024
Meta logo
Meta
Medium
Software Engineer

Handle last-minute track change

Behavioral Question: Switching Tracks Mid-Interview Loop You are midway through a software engineering onsite when the interview focus changes (e.g., ...

Behavioral & Leadership
2
0
34 people solved
Aug 14, 2025
Meta logo
Meta
Medium
Software Engineer

Find missing ranges in interval

Given a sorted unique integer array nums and two integers lower and upper defining an inclusive interval [lower, upper], return all missing ranges not...

Coding & Algorithms
2
0
31 people solved
Aug 14, 2025
Meta logo
Meta
Medium
Data Engineer

Solve four algorithmic library problems

Solve the following coding tasks: 1) Maximum Points from Different Categories: Given an array of items (category, points) and an integer k, choose exa...

Coding & Algorithms
9
0
87 people solved
Aug 13, 2025
Meta logo
Meta
Medium
Software Engineer

Design core bank operations

Design core bank operations Design a BankSystem Class (Take‑home Project) Goal Design and document a BankSystem class that supports three operations: ...

System Design
3
0
34 people solved
Aug 9, 2025
Meta logo
Meta
Medium
Software Engineer

Design a mini banking system

Design a mini banking system In-Memory Banking System — Design and Implementation You are to design and implement an in-memory banking system that sup...

System Design
9
0
86 people solved
Aug 7, 2025
Meta logo
Meta
Medium
Software Engineer

Check near-palindrome with one deletion

Check near-palindrome with one deletion Given a string s, determine whether it can become a palindrome after deleting at most one character. Return bo...

Coding & Algorithms
3
0
28 people solved
Aug 7, 2025
Meta logo
Meta
Medium
Data Scientist

[Analytical Reasoning] Comparing Two Newsfeed Ad Insertion Methods

Compare two ad-insertion methods for a 100-post newsfeed. Both methods have the same average ad load. - Method A: each post is independently replaced ...

Analytics & Experimentation
18
0
77 people solved
Apr 7, 2025
Meta logo
Meta
Easy
Data ScientistSenior+ Locked

Design metrics to detect harmful content and fraud

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a rea...

Analytics & Experimentation
3
0
30 people solved
Aug 5, 2025
Meta logo
Meta
Medium
Software Engineer

Solve linked-list and top-K algorithm tasks

Question Given a singly linked list, return the k-th node counted from the end. Given an integer array, return the top k most frequent numbers. Given ...

Coding & Algorithms
6
0
52 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Quantify Latent Demand for Group Video Calling Feature

Quantify Latent Demand for Group Video Calling Feature Scenario A consumer messaging app is preparing to launch group video calling. You have access t...

Analytics & Experimentation
3
0
25 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Probability of Fourth Good Response After Three Successes

Determine Probability of Fourth Good Response After Three Successes Evaluating Good-Response Rates for Chatbot Outputs Context You are evaluating chat...

Statistics & Math
2
0
42 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Key Metrics for Notification System Success

Analyze Key Metrics for Notification System Success Scenario You are evaluating a new push-notification system for a social app. The goal is to determ...

Analytics & Experimentation
3
0
47 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate and Compare Survey Response Rates for User Tenure

Surveys +--------+------------+--------------+----------+ | userid | date | survey_event | response | +--------+------------+--------------+----...

Data Manipulation (SQL/Python)
1
0
12 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Influence Stakeholders for Product Decision at Meta

Influence Stakeholders for Product Decision at Meta Behavioral: Influencing Stakeholders To Drive a Product Decision Scenario Cross-functional product...

Behavioral & Leadership
4
0
35 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Investigate Causes of Decline in Facebook Group Comments

Investigate Causes of Decline in Facebook Group Comments Scenario A sharp decline in Comments per Post (CPP) was observed in Facebook Groups last week...

Analytics & Experimentation
2
0
33 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Metrics to Detect Fake-Account Activity on Facebook

Identify Metrics to Detect Fake-Account Activity on Facebook Detecting and Measuring Fake Accounts Scenario Facebook wants to understand and curb fake...

Statistics & Math
3
0
53 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Determine Key Metrics and Design A/B Test for Ad Ranking

Determine Key Metrics and Design A/B Test for Ad Ranking Experiment Design: Replacing Rule-Based Ad Ranking with a Recommender Context You are launchi...

Analytics & Experimentation
5
0
43 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Design A/B Test to Evaluate New Video-Feed Feature

Design A/B Test to Evaluate New Video-Feed Feature Scenario A consumer social-media app is launching a short‑video feed (TikTok-style). A newly added ...

Analytics & Experimentation
2
0
28 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.

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