Meta Interview Questions

Meta Behavioral & Leadership Interview Questions

Practice 1,166 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 Company08.08.2026
Showing 20 results
Role
Meta logo
Meta
Medium
Data Scientist Locked

Evaluate fake accounts and ad creation

This question evaluates a data scientist's competencies in measurement and experimentation, covering prevalence estimation and detection system evalua...

Analytics & Experimentation
2
0
43 people solved
Feb 9, 2026
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Meta
Medium
Data Scientist Locked

Evaluate Notification-Based Account Ranking

This question evaluates a data scientist's competency in causal inference, A/B test and experiment design, metric definition and selection, statistica...

Analytics & Experimentation
3
0
23 people solved
Feb 9, 2026
Meta logo
Meta
Medium
Product Manager

Describe Tough Project and Tight Deadline

Behavioral Prompt: Challenging Project and Tight Timeline Tell me about your most challenging project. Also describe a time when you had to deliver un...

Behavioral & Leadership
6
0
45 people solved
Jul 1, 2025
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Meta
Medium
Machine Learning Engineer AI Locked

Maximize Unique Letters

This question evaluates proficiency in combinatorial optimization, bit manipulation, and state-compression techniques for selecting subsets under uniq...

Coding & Algorithms
2
0
35 people solved
Feb 8, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Solve maze reachability and two follow-ups

This set of tasks evaluates graph and state-space search with constrained movement (maze rolling ball), tree algorithms and recursion for computing di...

Coding & Algorithms
12
0
97 people solved
Feb 7, 2026
Meta logo
Meta
Medium
Software Engineer

Compute interval mode, BST range sum, exclusive time

You are given several independent coding tasks. A) Most frequent integer covered by intervals You are given an integer range [-M, M] and a list of inc...

Coding & Algorithms
2
0
40 people solved
Oct 2, 2025
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Meta
Easy
Product Analyst Locked

Write SQL for call pickup and usage metrics

This question evaluates proficiency in SQL data manipulation—specifically aggregation, joins, filtering, and safe handling of edge cases—to compute ca...

Data Manipulation (SQL/Python)
2
0
52 people solved
Feb 2, 2026
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Meta
Medium
Data Scientist

Evaluating the Impact of Duplicate and Stolen Posts on a Content Platform

Evaluating the Impact of Duplicate and Stolen Posts on a Content Platform You are a data scientist at a large user-generated-content platform (think a...

Analytics & Experimentation
0
0
10 people solved
Feb 1, 2026
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Meta
Medium
Data Scientist

Posts and Replies Engagement

Posts and Replies Engagement A content platform stores user-generated posts and the replies that those posts receive. You need to answer two questions...

Data Manipulation (SQL/Python)
0
0
8 people solved
Feb 1, 2026
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Meta
Easy
Software Engineer Locked

Compute Sliding Window Averages

This question evaluates proficiency in array manipulation, sliding-window techniques, and algorithmic efficiency including time and space complexity. ...

Coding & Algorithms
1
0
21 people solved
Jan 29, 2026
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Meta
Medium
Product Analyst Locked

Write Call Analytics SQL Queries

This question evaluates SQL data manipulation and analytical competencies, including aggregation, joins between user and event tables, time-window fil...

Data Manipulation (SQL/Python)
3
0
26 people solved
Jan 28, 2026
Meta logo
Meta
Medium
Data Scientist

Assess ranking change and design experiment

A multi-account product currently orders a user's accounts by most recent visit. The product team wants to change the ranking so that accounts with th...

Analytics & Experimentation
3
0
47 people solved
Jan 21, 2026
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Meta
Medium
Data Scientist

How would you evaluate Pixel issue alerts?

Meta is considering a new advertiser-facing ad management feature. When the system detects that an advertiser's Ads Pixel may be misconfigured or send...

Analytics & Experimentation
2
0
25 people solved
Jan 20, 2026
Meta logo
Meta
Medium
Data Scientist

Write SQL for Pixel Signal Metrics

You are working on Meta Ads Pixel analytics. Assume all timestamps are stored in UTC, and analyze the last 30 complete calendar days. Tables 1. advert...

Data Manipulation (SQL/Python)
4
0
56 people solved
Jan 20, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Maximize score in 15-sum card game

This question evaluates algorithmic problem-solving in combinatorial search, state-space optimization, and memoization by asking for the maximum achie...

Coding & Algorithms
9
0
110 people solved
Jan 18, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design coding platform with global leaderboard

This question evaluates proficiency in scalable distributed systems and system design, including real-time ranking algorithms, data modeling, API desi...

System Design
6
0
57 people solved
Jan 18, 2026
Meta logo
Meta
Medium
Software Engineer

Solve word count, node, segmentation, stock tasks

You have four tasks: 1) Count words with explicit rules: Given a text document, return the number of words. First define precisely what qualifies as a...

Coding & Algorithms
3
0
41 people solved
Sep 6, 2025
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Meta
Medium
Data Engineer

Compute capacities after site closures

You are given a nested dictionary redistribution where redistribution[closed_site][dest_site] equals the additional capacity required at dest_site if ...

Coding & Algorithms
6
0
72 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Data Engineer

Compute missing letters to form original string

Implement a function that, given two strings original and typed (typed is a misspelled/partial version of original), returns the number of additional ...

Coding & Algorithms
8
1
64 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Generate unique permutations with duplicates

Given an array of integers that may contain duplicates, generate all unique permutations. Ensure no duplicate permutations are returned even if some v...

Coding & Algorithms
7
0
56 people solved
Sep 6, 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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