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
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Meta
Medium
Data Scientist

Evaluate Probability of Positive User Comments and Model Performance

Evaluate Probability of Positive User Comments and Model Performance Social-Media Positivity: Independence and Model Comparison Context You are evalua...

Statistics & Math
107
0
385 people solved
Aug 4, 2025
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Meta
Medium
Machine Learning Engineer

Solve matrix diagonal and sliding-window statistics

Solve matrix diagonal and sliding-window statistics 1) Given an m x n integer matrix, determine whether every top-left to bottom-right diagonal has th...

Coding & Algorithms
4
0
75 people solved
Jul 31, 2025
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Meta
Medium
Software Engineer

Design Ticket Booking Auto Release

Design Ticket Booking Auto Release System Design: Auto-expiring Ticket Reservations Problem Design a ticket-booking system where a reserved ticket aut...

System Design
11
0
41 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Design keyword search for social posts

Design a Facebook-like post search feature: - Input: one or more keywords. - Output: all (or top-K) posts that contain the keyword(s). - Assume posts ...

System Design
4
0
32 people solved
Oct 2, 2025
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Meta
Easy
Software Engineer Locked

Implement array parity and tree level views

This question evaluates proficiency in array frequency analysis and binary tree traversal, testing skills in data structures, correctness reasoning, a...

Coding & Algorithms
5
0
39 people solved
Mar 24, 2026
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Meta
Medium
Software Engineer

Handle priority conflicts, setbacks, and initiatives

Behavioral Prompts Answer using a specific past experience (STAR format recommended). 1) Conflict on Priority - Tell me about a time when you and a st...

Behavioral & Leadership
5
0
55 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer Locked

Design a Netflix-like video streaming service

This question evaluates competency in designing large-scale streaming platforms, focusing on distributed systems, content storage and encoding pipelin...

System Design
4
0
51 people solved
Dec 15, 2025
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Meta
Hard
Data Engineer

Define and validate product metrics

Define and validate product metrics End-to-End Analytics Design for a New Product Feature Context: You are the data engineer partnering with product, ...

Analytics & Experimentation
3
0
41 people solved
Jul 15, 2025
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Meta
Easy
Data Scientist

Expected impressions per user under random assignment

Random Assignment of Ad Impressions Across Users In an A/B experiment, Y ad impressions are served uniformly at random across X distinct users. Each i...

Analytics & Experimentation
15
0
35 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Evaluate the Health of Facebook Groups

Group Health Metrics and Threaded Comments Experiment You are a Data Scientist working on a platform with Groups ranging from small hobby clubs to ver...

Analytics & Experimentation
52
0
54 people solved
Jul 12, 2025
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Meta
Easy
Data Scientist

Calculate Posterior Probability of Flagged User Being Bad Actor

Calculate Posterior Probability of a Flagged User Being a Bad Actor A platform runs a binary classifier that flags users who might be bad actors. You ...

Statistics & Math
107
1
274 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

How to Validate Friends' Content Engagement Hypothesis?

Validate Friends' Content Engagement Hypothesis A Meta product team wants to know whether content from a viewer's friends or connected authors drives ...

Analytics & Experimentation
102
0
205 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Design "Restaurants You May Know" Recommendation Algorithm

Design "Restaurants You May Know" Recommendation Algorithm A food-delivery app wants to launch a personalized home-page module called "Restaurants You...

Analytics & Experimentation
36
0
130 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Analyze User Transfer Distribution in Initial Launch Period

Analyze User Transfer Distribution in an Initial Launch Period A new peer-to-peer payments feature has launched. You are asked to analyze the number o...

Statistics & Math
26
0
64 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Describe Handling Unexpected Changes and Data-Driven Conflicts

Describe Handling Unexpected Changes and Data-Driven Conflicts This behavioral interview prompt assesses cultural fit, ownership, communication, adapt...

Behavioral & Leadership
28
0
125 people solved
Jul 12, 2025
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Meta
Hard
Data Scientist

Identify User Interest in Group Video Calls Using Data

Identify User Interest in Group Video Calls Using Data You are designing and analyzing a new group video-calling feature for a large social or messagi...

Analytics & Experimentation
175
3
304 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Superiority of Model A Using Hypothesis Testing

Hypothesis Test: Is Model A Better Than Model B? A search feature marks a session as successful only when both relevancy and accuracy binary flags equ...

Statistics & Math
26
0
104 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Ad Insertion Statistics for Two Methods

Ad Insertion Statistics for Two Feed Strategies You are comparing two ways of inserting ads into a 100-post feed. - Option A: Each post independently ...

Statistics & Math
73
0
81 people solved
Jul 12, 2025
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Meta
Medium
Product Analyst Locked

Write SQL for call metrics

This question evaluates a candidate's competency in SQL-based data manipulation and analytics, specifically aggregations, JOINs across relational tabl...

Data Manipulation (SQL/Python)
4
0
47 people solved
Mar 19, 2026
Meta logo
Meta
Medium
Software Engineer

Describe resolving conflict by persuading others

Describe a time you had a significant work-related conflict or disagreement with a teammate, stakeholder, or manager where you believed your approach ...

Behavioral & Leadership
2
0
55 people solved
Dec 8, 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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