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

Analyze Top Call Initiators and Active French Video Callers

calls +---------+-----------+-------------+---------------------+---------+-----------+ | call_id | caller_id | receiver_id | call_start_time | co...

Data Manipulation (SQL/Python)
87
0
229 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Explain Type I vs. Type II Errors in A/B Testing

Explain Type I vs. Type II Errors in A/B Testing A/B Testing Errors and Estimation Under Skewed Metrics Context You are analyzing an A/B experiment fo...

Statistics & Math
20
0
57 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Data Scientist

Calculate Probability of Honest and Relevant Chatbot Answers

Calculate Probability of Honest and Relevant Chatbot Answers Chatbot Evaluation: Honesty and Relevance Scenario You are evaluating a customer-service ...

Statistics & Math
24
0
56 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

How would you measure Group Call success?

You are interviewing for a Data Scientist role at a social communication product similar to Meta. The team asks you to evaluate a Group Call feature t...

Analytics & Experimentation
2
0
37 people solved
Dec 26, 2025
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Meta
Hard
Data ScientistSenior+ Locked

Prove high-quality pixels improve ad performance

Prove high-quality pixels improve ad performance evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and...

Analytics & Experimentation
2
0
31 people solved
Aug 1, 2025
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Meta
Medium
Data Engineer

Discuss conflicts, deadlines, and persuasion

Discuss conflicts, deadlines, and persuasion Behavioral and Leadership Prompts for a Data Engineer (Onsite) Context: You are interviewing for a Data E...

Behavioral & Leadership
2
0
44 people solved
Aug 1, 2025
Meta logo
Meta
Hard
Software Engineer

Design leaderboard and messenger systems

Design leaderboard and messenger systems This Meta onsite system-design round asks you to design two large-scale systems back to back. Cover end-to-en...

System Design
7
0
45 people solved
Jul 31, 2025
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Meta
Medium
Software Engineer

Solve linked list, top-K, and string reduction

Solve linked list, top-K, and string reduction Solve the following algorithmic tasks: 1) Given a singly linked list, return the k-th node from the end...

Coding & Algorithms
5
0
39 people solved
Jul 31, 2025
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Meta
Medium
Machine Learning Engineer

Implement sliding-window moving average

Design a class MovingAverage that supports a constructor MovingAverage(k) and a method next(val) returning the average of the last k values from a dat...

Coding & Algorithms
5
2
46 people solved
Jul 31, 2025
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Meta
Medium
Software Engineer

Explain behavioral experiences and decisions

Explain behavioral experiences and decisions Behavioral Interview Prompts — Onsite (Software Engineer) Context You are preparing for the onsite behavi...

Behavioral & Leadership
18
0
90 people solved
Jul 29, 2025
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Meta
Hard
Software Engineer

Design coding platform leaderboard system

Design coding platform leaderboard system System Design: Scalable Coding Platform with Live Global Leaderboard Context Design a coding challenge platf...

System Design
15
0
83 people solved
Jul 29, 2025
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Meta
Medium
Software Engineer

Compute product of array except self

Given an integer array nums, return an array out where out[i] equals the product of all elements in nums except nums[i]. Do it in O(n) time using cons...

Coding & Algorithms
2
0
24 people solved
Jul 29, 2025
Meta logo
Meta
Hard
Data Scientist

Define metrics for high-quality notifications

Define metrics for high-quality notifications Context You are a Data Scientist partnering with a product team at Facebook/Meta that owns push/in-app n...

Analytics & Experimentation
3
0
38 people solved
Jul 27, 2025
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Meta
Easy
Data Scientist Locked

Design metrics and experiment for stolen-post detection

Evaluates skills in metrics design, diagnostic analysis, and online experiment methodology within Analytics & Experimentation for a Data Scientist pos...

Analytics & Experimentation
13
0
91 people solved
Dec 18, 2025
Meta logo
Meta
Easy
Data Scientist

Compute reply-based user metrics in 7 days

You are analyzing discussions on a social platform. Tables all_post - post_id (BIGINT, PK) - post_author_id (BIGINT, FK → user.user_id) - post_creatio...

Data Manipulation (SQL/Python)
21
1
174 people solved
Dec 18, 2025
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Meta
Medium
Data Engineer

Demonstrate ownership and conflict resolution

Demonstrate ownership and conflict resolution Behavioral: 0→1 Data Initiative, Prioritization, and Cross-Functional Leadership Context: Onsite intervi...

Behavioral & Leadership
6
0
48 people solved
Jul 15, 2025
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Meta
Medium
Software Engineer

Discuss achievements, ambiguity handling, growth, and conflict management

Discuss achievements, ambiguity handling, growth, and conflict management Behavioral & Leadership Interview Prompts (Software Engineer — Onsite) Conte...

Behavioral & Leadership
2
0
27 people solved
Jul 15, 2025
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Meta
Medium
Machine Learning Engineer Locked

Design comment ranking for a news feed

This question evaluates a candidate's ability to design an ML-powered comment-ranking system, testing competencies in personalization, engagement and ...

ML System Design
6
0
99 people solved
Dec 15, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Answer impact, conflict, and difficult coworker questions

Behavioral questions 1. Describe the most impactful project you have worked on. 2. Tell me about a difficult person you have worked with. 3. Describe ...

Behavioral & Leadership
4
0
62 people solved
Dec 15, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Compute nested depth sum and grid distance

Problem A: Weighted sum of integers in a nested list You are given a nested list structure that may contain integers or other nested lists. Define the...

Coding & Algorithms
9
0
68 people solved
Dec 15, 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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