Meta Interview Questions

Meta System Design 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 Company07.06.2026
Showing 20 results
Role
Meta logo
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
61
5
412 people solved
Nov 20, 2025
Meta logo
Meta
Medium
Software EngineerSenior+

Prepare Leadership And Collaboration Stories

Prepare concise behavioral interview answers for the following prompts. Use specific examples from your own experience, quantify impact where possible...

Behavioral & Leadership
2
0
32 people solved
Apr 9, 2026
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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
Meta logo
Meta
Medium
Site Reliability Engineer Locked

Troubleshoot a Midnight Web Server Outage

This question evaluates a candidate's incident response, systems debugging, and root-cause analysis skills, focusing on log-driven investigation, Linu...

Software Engineering Fundamentals
14
0
106 people solved
Apr 6, 2026
Meta logo
Meta
Medium
Software Engineer

Design an online auction system

Design an online auction system (eBay-style). Requirements: - Users create auctions with start/end time and starting price. - Users place bids; system...

System Design
8
0
61 people solved
Feb 25, 2026
Meta logo
Meta
Medium
Software Engineer

Find Any Local Minimum with Iterative Binary Search

Find Any Local Minimum with Iterative Binary Search Given a nonempty integer array numbers, return the index of any local minimum. An index i is a loc...

Coding & Algorithms
0
0
13 people solved
Mar 2, 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
6
0
59 people solved
Mar 19, 2026
Meta logo
Meta
Medium
Software Engineer

Design an Expected O(1) Randomized Container

Design an Expected O(1) Randomized Container Design a generic container that stores distinct elements and supports: - insert(element) -> bool: add an ...

Software Engineering Fundamentals
0
0
15 people solved
Mar 2, 2026
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Meta
Hard
Machine Learning Engineer

Design Nearby and Notification Ranking

Two machine learning system design prompts were mentioned: 1. Nearby place recommendation for a mobile user Design a real-time recommendation syste...

ML System Design
11
0
159 people solved
Jan 30, 2026
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Meta
Medium
Software Engineer

Describe conflict resolution and initiative

The behavioral round reportedly consisted of 7-8 standard questions. A polished version of the expected prompts would be: - Tell me about a time you h...

Behavioral & Leadership
5
0
73 people solved
Mar 4, 2026
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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
6
0
43 people solved
Mar 4, 2026
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Meta
Medium
Software Engineer

Count Distinct Values in a Sorted Array When K Is Small

Count Distinct Values in a Sorted Array When K Is Small Given an integer array sorted in nondecreasing order, return the number of distinct values. Le...

Coding & Algorithms
1
0
12 people solved
Mar 2, 2026
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Meta
Medium
Machine Learning Engineer Locked

Design a Short-Video Recommendation System

This question evaluates competency in designing large-scale short-video recommendation systems, including machine learning model selection, candidate ...

ML System Design
8
0
86 people solved
Feb 28, 2026
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Meta
Hard
Machine Learning Engineer

Discuss Projects, Failures, and Growth

Prepare structured answers for the following behavioral prompts from an interview: - Describe the project you are most proud of. - What was the hardes...

Behavioral & Leadership
14
0
112 people solved
Jan 30, 2026
Meta logo
Meta
Medium
Software Engineer

Select words to maximize unique characters

Problem You are given an array of strings words. You may choose any subset of these strings and concatenate the chosen strings in any order. Your goal...

Coding & Algorithms
38
0
504 people solved
Dec 15, 2025
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Meta
Easy
Data Scientist Locked

How would you evaluate pixel-issue notifications?

This question evaluates a data scientist's skills in experimentation design, metric framework development, causal inference, and measurement-aware ana...

Analytics & Experimentation
10
0
98 people solved
Feb 18, 2026
Meta logo
Meta
Easy
Data Scientist

How would you define and use retention metrics?

Scenario You are a Data Scientist supporting a consumer product (app or website). A PM asks you to “dive deep” on user retention and recommends tracki...

Analytics & Experimentation
13
0
176 people solved
Feb 18, 2026
Meta logo
Meta
Easy
Data Scientist

How to measure harmful-content severity and run experiments

Question You are a Data Scientist working on content integrity / harmful content at a large social media platform (e.g., hate/harassment, self-harm, g...

Analytics & Experimentation
39
0
255 people solved
Feb 18, 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
13 people solved
Jun 20, 2026
Meta logo
Meta
Medium
Software EngineerSenior+ AI

Build a FastAPI summarization service

Implement a minimal but production-ready FastAPI service for text summarization. Requirements: - Expose an endpoint POST /summarize. - The request bod...

Coding & Algorithms
5
0
51 people solved
Jan 19, 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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