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
Meta logo
Meta
Easy
Data ScientistSenior+

Explain why IG Story usage exceeds Facebook

Explain why IG Story usage exceeds Facebook Product analytics case: Instagram vs Facebook Stories You work on Stories across two apps: Instagram (IG) ...

Analytics & Experimentation
7
0
64 people solved
Aug 5, 2025
Meta logo
Meta
Medium
Software Engineer

Solve sliding window and tree BFS

Solve sliding window and tree BFS Solve a typical medium-level sliding-window problem (e.g., longest substring without repeating characters). LeetCode...

Coding & Algorithms
5
0
51 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Analyze private-account product metrics

Analyze private-account product metrics A social network is building (or refining) a private account feature: any user can set their account to privat...

Analytics & Experimentation
6
1
78 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Software Engineer

Debug and optimize React performance issues

Debug and optimize React performance issues React Debugging and Performance Assessment Background You're reviewing a React single-page application dur...

System Design
4
0
49 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Influence Stakeholders for Product Decision at Meta

Influence Stakeholders for Product Decision at Meta Behavioral: Influencing Stakeholders To Drive a Product Decision Scenario Cross-functional product...

Behavioral & Leadership
4
0
37 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Ensure Effective Teamwork Amid Conflicting Stakeholder Opinions

Ensure Effective Teamwork Amid Conflicting Stakeholder Opinions Behavioral: Cross-Functional Collaboration, Miscommunication, and Conflict Handling Co...

Behavioral & Leadership
5
0
49 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Design A/B Test to Evaluate Payment Method Impact

Design A/B Test to Evaluate Payment Method Impact A/B Experiment Design: New Payment Method Rollout Context You are evaluating whether to launch a new...

Analytics & Experimentation
6
0
38 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Analyze Mobile Promo Orders with SQL Query and Metrics

orders +-----------+---------+--------------+------------+-----------+----------+ | order_id | user_id | order_amount | order_date | is_mobile | is_p...

Data Manipulation (SQL/Python)
2
0
12 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Assess Cultural Fit and Problem-Solving in Reality Labs Role

Assess Cultural Fit and Problem-Solving in Reality Labs Role Behavioral and Leadership Interview Prompts (Data Scientist, Reality Labs) Context The hi...

Behavioral & Leadership
4
0
36 people solved
Aug 4, 2025
Meta logo
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
3
0
45 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Analyze Comment Distribution Using Statistical Metrics and Tests

Analyze Comment Distribution Using Statistical Metrics and Tests Assessing Concentration of Comments Across Posts Scenario You are analyzing comments ...

Analytics & Experimentation
4
0
36 people solved
Aug 4, 2025
Meta logo
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
6
0
47 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze View Distribution and Recommendation Overlap in Videos

Analyze View Distribution and Recommendation Overlap in Videos Short-Video Platform: View Distribution and Recommendation Overlap Context You are anal...

Statistics & Math
7
0
54 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Define Product Metrics: Align Stakeholders, Measure Success, Improve Results

Define Product Metrics: Align Stakeholders, Measure Success, Improve Results Behavioral Question: Defining New Product Metrics Without Clear Guidance ...

Behavioral & Leadership
18
0
91 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Influence Stakeholders Without Authority: Strategies and Outcomes

Influence Stakeholders Without Authority: Strategies and Outcomes Scenario Meta Data Scientist onsite behavioral & leadership loop. The interviewer pr...

Behavioral & Leadership
22
0
99 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Track Metrics to Measure Push Notification Quality

Track Metrics to Measure Push Notification Quality Scenario A consumer mobile app sends push notifications to drive user engagement. You need to evalu...

Analytics & Experimentation
23
0
49 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Describe Handling Conflict in Team Projects and Collaboration

Describe Handling Conflict in Team Projects and Collaboration Behavioral & Leadership (Onsite) — Data Scientist Scenario You are interviewing for a Da...

Behavioral & Leadership
89
0
231 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Design visualizations for streaming metrics

Design visualizations for streaming metrics Design a Monitoring and Diagnosis Visualization for a Video-Streaming Metric Context You are building an o...

Analytics & Experimentation
6
0
50 people solved
Aug 1, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Describe learning from a post-interview bug

Describe learning from a post-interview bug Behavioral Prompt: Bug Discovered After a Remote Technical Screen Context You are in a remote technical sc...

Behavioral & Leadership
8
0
67 people solved
Jul 31, 2025
Meta logo
Meta
Easy
Data Scientist Locked

Compute Bayes probability for fake accounts

This question evaluates Bayesian reasoning and probabilistic modeling skills, including conditional probability, base-rate effects, detector character...

Statistics & Math
14
1
105 people solved
Nov 1, 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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