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
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Meta
Medium
Machine Learning Engineer

Check diagonal and compute window statistics

Question Given a square matrix, determine whether all elements on its main diagonal are identical. LeetCode 346. Moving Average from Data Stream – des...

Coding & Algorithms
9
0
69 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Solve UTF-8 Validation & Shortest Path

Question LeetCode 393. UTF-8 Validation LeetCode 1091. Shortest Path in Binary Matrix https://leetcode.com/problems/utf-8-validation/description/ http...

Coding & Algorithms
2
0
37 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Validate carpool capacity

Question LeetCode 1094. Car Pooling – Given trips[i] = [numPassengers, start, end] and an integer capacity, return true if the vehicle can fulfill all...

Coding & Algorithms
5
0
68 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Solve linked-list and top-K algorithm tasks

Question Given a singly linked list, return the k-th node counted from the end. Given an integer array, return the top k most frequent numbers. Given ...

Coding & Algorithms
6
0
51 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Retrieve Ad Metrics and Rates for Last 7 Days

ads +--------+---------------+----------+ | ad_id | advertiser_id | ad_type | +--------+---------------+----------+ | 101 | 1001 | image...

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

Resolve Poor Team Collaboration: Identify Issues, Implement Solutions

Resolve Poor Team Collaboration: Identify Issues, Implement Solutions Behavioral & Leadership Interview (Data Scientist) Scenario You are interviewing...

Behavioral & Leadership
3
0
23 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Overcome Challenges and Build Trust in Teamwork

Overcome Challenges and Build Trust in Teamwork Behavioral Interview: Teamwork, Feedback, Trust, and Conflict (Data Scientist) Context You are intervi...

Behavioral & Leadership
2
0
44 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Identify Top Three Active Users by Event Date

event_log +------------+---------+-----------+---------------------+ | event_date | user_id | event_type| event_timestamp | +------------+--------...

Data Manipulation (SQL/Python)
137
1
356 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Analyze Central Limit Theorem in User Comment Distribution

Analyze Central Limit Theorem in User Comment Distribution Comments per User — CLT, Expectation, SD, and 95% CI Context You are measuring how many com...

Statistics & Math
111
0
319 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Convince PM to Implement Duplicate Observation Tool

Convince PM to Implement Duplicate Observation Tool Scenario Meta is considering building a Duplicate Observation Tool (DOT) to detect malicious copy‑...

Analytics & Experimentation
4
0
31 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Evaluate New Ad Model with A/B Testing Experiment

Evaluate New Ad Model with A/B Testing Experiment Evaluate a New Ads Recommendation Model via Online Experimentation Scenario You have trained a new a...

Analytics & Experimentation
2
0
42 people solved
Aug 4, 2025
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Meta
Easy
Data Scientist

Calculate Conversion Probability for Male Ad Impressions

Calculate Conversion Probability for Male Ad Impressions Scenario You are estimating conversion probabilities for ad impressions. Before knowing a use...

Statistics & Math
27
0
82 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Determine User Need for In-App Video Call Feature

Determine User Need for In-App Video Call Feature Scenario A consumer messaging app is considering launching an in-app Video Call feature. You have ac...

Analytics & Experimentation
2
0
27 people solved
Aug 4, 2025
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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)
86
0
227 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Identify Algorithms for Detecting Malicious Duplicated Content

Identify Algorithms for Detecting Malicious Duplicated Content Detecting Malicious Duplicated Text (DOT) Scenario You are selecting technical approach...

Machine Learning
5
0
45 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Design a System to Recommend Local Restaurant Profiles

Design a System to Recommend Local Restaurant Profiles Recommending Local Restaurant Pages in the News Feed Context Design a non-ads recommendation sy...

Machine Learning
3
0
47 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Quantify Latent Demand for Group Video Calling Feature

Quantify Latent Demand for Group Video Calling Feature Scenario A consumer messaging app is preparing to launch group video calling. You have access t...

Analytics & Experimentation
3
0
24 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Improve YoY Revenue Analysis with Complementary Metrics

ads_revenue +------------+-----------+ | date | revenue | +------------+-----------+ | 2023-01-01 | 120000 | | 2023-02-01 | 125500 | | 2...

Data Manipulation (SQL/Python)
101
0
240 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Generate Daily Unique User Views for Each Shop

shop_views +---------+---------+---------------------+ | user_id | shop_id | view_time | +---------+---------+---------------------+ | 101 ...

Data Manipulation (SQL/Python)
69
0
253 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Choose Randomization Unit and Mitigate Network Effects

Choose Randomization Unit and Mitigate Network Effects A/B Test Design for a New Messenger Feature with Network Effects Context You are designing an A...

Statistics & Math
4
0
36 people solved
Aug 4, 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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