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
Product Manager

Parking-Spot Finder on Google Maps

Product Design Prompt: Parking-Spot Finder Integrated with Google Maps Design a parking-spot finder feature integrated into Google Maps. Address: 1. T...

Product / Decision Making
7
0
65 people solved
Jul 4, 2025
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Meta
Medium
Backend Engineer Locked

Design an Instagram-like platform

This question evaluates proficiency in scalable backend architecture, distributed systems, data modeling, media storage and processing, API design, fe...

System Design
4
0
48 people solved
Apr 8, 2026
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Meta
Medium
Backend Engineer AI Locked

Solve coding and AI-assisted tasks

This set evaluates algorithmic problem-solving across arrays, immutable linked-list interfaces, parent-pointer tree traversal, string validity via min...

Coding & Algorithms
8
0
66 people solved
Apr 8, 2026
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Meta
Easy
Data Scientist

How would you compare Facebook vs Instagram Stories?

You work on short-form ephemeral content. Both Facebook Stories and Instagram Stories exist, and leadership asks: Which product should we invest in, a...

Analytics & Experimentation
12
0
100 people solved
Nov 1, 2025
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Meta
Easy
Data Scientist

Compute probabilities for chatbot response quality

Context A chatbot response is considered good if it is both: - Helpful, and - Honest. You are told: - \(P(\text{Helpful}) = 0.8\) - \(P(\text{Honest})...

Statistics & Math
4
1
89 people solved
Oct 30, 2025
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Meta
Medium
Site Reliability Engineer Locked

Implement a Streaming VMStat Alert

This question evaluates streaming data processing skills, sliding-window counting algorithms, and efficient online state management for metric monitor...

Coding & Algorithms
5
0
74 people solved
Apr 6, 2026
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Meta
Medium
Data Scientist

Count unconnected posts and reactions

You are analyzing a newly launched feed feature intended to improve engagement by showing more unconnected content. Assume the following tables: - pos...

Data Manipulation (SQL/Python)
22
2
201 people solved
Apr 5, 2026
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Meta
Medium
Software Engineer Locked

Solve delimiter and CSV tasks

This question evaluates skills in string parsing and bracket balancing, CSV/file parsing and joining, computation of derived values, and sorting, emph...

Coding & Algorithms
6
0
53 people solved
Apr 5, 2026
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Meta
Medium
Software Engineer

Describe failures, self-reflection, and conflict resolution

Answer the following behavioral prompts with recent examples: 1. Self-reflection / improvement - “In a recent project, where could you have done be...

Behavioral & Leadership
16
0
122 people solved
Jan 6, 2026
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Meta
Medium
Software Engineer Locked

Find shortest path in an OOP maze

This question evaluates graph traversal and shortest-path reasoning within an object-oriented codebase, focusing on state representation, neighbor exp...

Coding & Algorithms
2
0
39 people solved
Jan 5, 2026
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Meta
Hard
Software Engineer

Design an online coding judge

Question Design an online coding practice and judging platform (think LeetCode / HackerRank / Codeforces) where users submit solutions that are compil...

System Design
5
0
46 people solved
Aug 14, 2025
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Meta
Medium
Software Engineer Locked

Implement fast power and k-palindrome

This question evaluates numerical algorithm design and string algorithm skills, focusing on efficient exponentiation with negative exponents and deter...

Coding & Algorithms
5
0
44 people solved
Apr 1, 2026
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Meta
Medium
Software Engineer Locked

Design a flight search platform

This question evaluates understanding of distributed systems, API and data model design, large-scale data ingestion and indexing, search ranking and c...

System Design
1
0
27 people solved
Oct 17, 2025
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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
37 people solved
Aug 9, 2025
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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
89 people solved
Aug 7, 2025
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Meta
Hard
Data Scientist

Measure impact of bot mitigation via experiment

Experiment Design: Measuring the Impact of a Bot‑Mitigation System Context You are evaluating a production change to a large social platform that hide...

Analytics & Experimentation
9
0
101 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Design bot detection and evaluate trade-offs

Bot-Detection System Design for Comment Activity Context You are designing and evaluating a machine learning system to detect automated (bot) comment ...

Machine Learning
3
0
44 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist Locked

Estimate bots and CI from DAU spike

This question evaluates proficiency in mixture modeling for anomaly detection, parametric and nonparametric inference for mean differences, handling o...

Statistics & Math
9
1
98 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Measure a friend-recommendation launch

A new friend-recommendation algorithm ships behind a feature flag. Design how you will measure success and decide whether to launch: - State no more t...

Analytics & Experimentation
4
0
70 people solved
Oct 13, 2025
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Meta
Medium
Data Scientist

Run a clean A/B test for recommendations

You must run an A/B test to evaluate the new hashtag recommender starting on 2025‑09‑01. 1) Define the randomization unit (user/session/impression) an...

Analytics & Experimentation
3
0
40 people solved
Oct 13, 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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