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
Hard
Data Scientist

Evaluate Messenger's P2P Payments Feature for Business Viability

Evaluate Messenger's P2P Payments Feature for Business Viability Facebook Messenger is considering launching a Venmo-like peer-to-peer money transfer ...

Analytics & Experimentation
85
0
252 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Choose Metrics for Evaluating Fake-User Classifier

Choose Metrics for Evaluating a Fake-User Classifier A sudden spike in daily average comments may be driven by fake users. You are asked to build a bi...

Machine Learning
19
0
51 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Revenue Shifts to Identify Cannibalization Effects

Analyze Revenue Shifts to Identify Cannibalization Effects Management observes strong revenue growth from one creation_source, such as a channel where...

Analytics & Experimentation
18
0
52 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Impact of Targeting Ads to High-Intent Users

Evaluate Impact of Targeting Ads to High-Intent Users A product manager proposes allocating all ad impressions to users predicted to be high intent, a...

Analytics & Experimentation
54
0
229 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Factors Before Replacing Recommendation Model

Evaluate Factors Before Replacing a Recommendation Model A large ads platform has built a new recommendation or ranking model and plans to deprecate t...

Machine Learning
63
0
276 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Instagram's Short-Video Recommender System Success

Evaluate Instagram's Short-Video Recommender System Success Instagram is launching a short-video recommender feed. You are asked to choose metrics, re...

Analytics & Experimentation
110
0
414 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Investigate Reasons for Higher Instagram Story Consumption

Investigate Reasons for Higher Instagram Story Consumption You observe that Stories are consumed more on Instagram than on Facebook. Assume Story cons...

Analytics & Experimentation
16
0
61 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Resolve Conflicts and Clarify Goals in Data Projects

Behavioral Interview: Conflict, Ambiguity, and Critical Feedback You are interviewing for a data-focused role. The interviewer is assessing collaborat...

Behavioral & Leadership
20
0
67 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Expected Meetings in Randomly Assigned Rooms

Expected Meetings in Randomly Assigned Rooms You are solving two probability questions about room occupancy. Constraints & Assumptions - In the first ...

Statistics & Math
85
0
242 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Design Experiment to Measure Shopping Feature Impact

Experiment Design: Measure Instagram Shopping Impact Instagram is launching an in-app Shopping feature, such as product tags, shop surfaces, or in-app...

Analytics & Experimentation
10
0
59 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Build Predictive Model for Buyer Engagement Uplift

Predict Engagement Uplift for a New "Show Similar Products" Button A new "Show similar products" button may change buyer engagement. You need to build...

Machine Learning
9
0
50 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Software Engineer AI Locked

Solve tree, array, and maze tasks

This set of problems evaluates proficiency in algorithms and data structures, covering binary tree diameter computation, array optimization for maximu...

Coding & Algorithms
2
0
26 people solved
Oct 17, 2025
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Meta
Medium
Machine Learning Engineer Locked

Design an image copyright-violation detection system

This question evaluates competency in designing scalable machine learning systems for image copyright detection, testing knowledge across computer vis...

ML System Design
15
0
152 people solved
Feb 12, 2026
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Meta
Medium
Machine Learning Engineer Locked

Compute the next lexicographic permutation

This question evaluates understanding of permutations and lexicographic ordering, in-place array manipulation, and algorithmic reasoning about time an...

Coding & Algorithms
7
0
65 people solved
Feb 12, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Extend BFS maze solver with keys and arrows

This question evaluates mastery of breadth-first search, state-space modeling for grid-based traversal, and handling augmented states like keys and on...

Coding & Algorithms
21
0
156 people solved
Feb 12, 2026
Meta logo
Meta
Medium
Machine Learning Engineer Locked

Maximize concatenation length with unique chars

This question evaluates understanding of string manipulation, set-based uniqueness constraints, and combinatorial subset selection for maximizing conc...

Coding & Algorithms
3
0
48 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design a live video comments system

This question evaluates skills in designing scalable, low-latency real-time systems, covering fan-out delivery, ordering and consistency semantics, du...

System Design
8
0
64 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Extend a BFS maze solver stepwise

This question evaluates proficiency in grid-based graph traversal and pathfinding, covering BFS mechanics, stateful reachability with keys and doors, ...

Coding & Algorithms
7
0
52 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software EngineerSenior+ Locked

Solve maze tasks and compute shortest routes

This multi-part question evaluates proficiency in grid-based pathfinding and shortest-path reasoning, debugging and incremental code extension, handli...

Coding & Algorithms
6
0
81 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer

Compute sparse dot product and count islands

You are asked to solve two coding problems. Problem 1: Sparse vector dot product Given two vectors A and B of the same length n (potentially large, e....

Coding & Algorithms
3
0
54 people solved
Feb 11, 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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