Meta Interview Questions

Meta Behavioral & Leadership 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 Company08.08.2026
Showing 20 results
Role
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Meta
Medium
Machine Learning Engineer

Design Place Recommendation System

Design a machine learning system for a maps or local-discovery product that recommends places a user may want to visit. The system should provide pers...

ML System Design
9
0
166 people solved
Mar 17, 2026
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Meta
Hard
Software EngineerSenior+ Locked

Design Top-K, Crawler, and Chess Systems

This question evaluates system-design competency across scalable distributed services, covering real-time streaming Top-K aggregation, large-scale dis...

System Design
10
0
111 people solved
Jun 2, 2026
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Meta
Easy
Data Scientist

How to evaluate a similar-listing notifications feature

Question You are a Data Scientist on a US C2C marketplace app (like Facebook Marketplace) where users buy and sell second-hand products. Current produ...

Analytics & Experimentation
94
1
796 people solved
Jan 17, 2026
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Meta
Medium
Site Reliability EngineerSenior+

Troubleshoot a production server outage

You are the on-call engineer responsible for a production server for the next several days. Discuss how you would approach the following: - How would ...

Software Engineering Fundamentals
26
0
226 people solved
Apr 12, 2026
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Meta
Medium
Software EngineerSenior+ AI

Solve Parser, Trading, Tree, And Deck Tasks

You will solve several independent programming tasks. For each task, implement clean, tested code and state the time and space complexity. 1. Validate...

Coding & Algorithms
5
0
37 people solved
Apr 9, 2026
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Meta
Medium
Software Engineer Locked

Palindrome After Deleting at Most One Character

This question evaluates a candidate's ability to reason about two-pointer string traversal and palindrome verification under a single edit constraint....

Coding & Algorithms
1
0
17 people solved
Jun 20, 2026
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Meta
Medium
Software Engineer Locked

Find Shortest Unique Prefixes

This question evaluates string-processing skills and understanding of data structures for prefix management (e.g., tries), along with algorithmic effi...

Coding & Algorithms
2
0
14 people solved
Jun 18, 2026
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Meta
Medium
Data Scientist

How should you evaluate unconnected content?

A social media platform has launched a feed feature that increases the share of unconnected content, meaning posts from creators who do not have an ex...

Analytics & Experimentation
14
0
146 people solved
Apr 5, 2026
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Meta
Medium
Data Scientist

Calculate Daily Survey Response Rates by Country

The interview report preserved the survey tables and the request to calculate response rate, but not the exact grouping or output contract. The follow...

Data Manipulation (SQL/Python)
30
4
353 people solved
Jul 6, 2026
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Meta
Medium
Data Scientist

Measure scheduled posts feature success

Facebook is considering launching a new feature that allows users to schedule a post to be published at a future time. The product hypothesis is that ...

Analytics & Experimentation
12
0
141 people solved
Apr 30, 2026
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Meta
Hard
Software Engineer AI

Design an Automated Ticket Investigation Agent

Design an AI-enabled agentic system that automatically investigates support or engineering tickets. The system should: - Read an incoming ticket and u...

ML System Design
8
0
75 people solved
Apr 27, 2026
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Meta
Medium
Data Engineer

Solve SQL and Python coding tasks

You are given a small library system with the following relational schema and several Python data-processing tasks. Answer the SQL questions and imple...

Coding & Algorithms
64
5
432 people solved
Nov 20, 2025
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Meta
Medium
Machine Learning EngineerSenior+

Answer senior-level behavioral interview questions

You are interviewing for a senior machine-learning engineer role on the tech-lead track at Meta, targeting roughly the IC6+ level. This is the first-r...

Behavioral & Leadership
22
0
186 people solved
Jan 28, 2026
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Meta
Medium
Data Scientist

Estimate ads ranking revenue impact

You are the data scientist for an ads ranking team at a large social platform. The team has built a new ranking algorithm for feed ads. The new model ...

Analytics & Experimentation
55
0
370 people solved
Apr 30, 2026
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Meta
Medium
Software EngineerSenior+ Locked

Design Search And Web Crawling Systems

This question evaluates system-design and distributed-systems competencies including large-scale indexing and query-serving, relevance ranking and per...

System Design
7
0
69 people solved
May 21, 2026
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Meta
Hard
Software Engineer

Design an Online Game Leaderboard

Question Design the backend system for an online game's leaderboard. The system should support: 1. Recording and updating player scores from game sess...

System Design
17
0
149 people solved
Apr 27, 2026
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Meta
Medium
Data Scientist Locked

Calculate CTR and ad revenue

This question evaluates proficiency in data manipulation and analytics, specifically metric calculation (CTR) and multi-currency revenue aggregation, ...

Data Manipulation (SQL/Python)
7
1
78 people solved
Jan 25, 2026
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Meta
Medium
Software Engineer Locked

Explain Collaboration, Ambiguity, and Prioritization

This question evaluates collaboration, ambiguity management, and prioritization competencies, focusing on interpersonal communication, stakeholder ali...

Behavioral & Leadership
3
0
53 people solved
May 14, 2026
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Meta
Medium
Product Analyst Locked

How would you grow Meta products?

This question evaluates product growth analytics and experimentation skills, including metric definition, funnel decomposition, segmentation, hypothes...

Analytics & Experimentation
7
0
65 people solved
Mar 19, 2026
Meta logo
Meta
Hard
Data Scientist

Should We Launch Group Calling?

Question You work on a consumer calling product (think Messenger/WhatsApp-style voice) that currently supports only one-to-one voice calls. The team i...

Analytics & Experimentation
7
0
53 people solved
Mar 4, 2026

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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