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 Scientist

Describe Handling Unexpected Feedback and Actions Taken

Describe Handling Unexpected Feedback and Actions Taken Behavioral: Resilience After Unexpected Negative Feedback or Rejection Context You are in an o...

Behavioral & Leadership
24
0
75 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate Instagram Shopping Feature's Revenue and Test Impact

Estimate Instagram Shopping Feature's Revenue and Test Impact Instagram Shopping: Sizing, Experiment Design, and Troubleshooting Context Instagram is ...

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

Measure Harmful Content Impact with Key Metrics

Measure Harmful Content Impact with Key Metrics Scenario A social-media platform needs to quantify how serious harmful or inappropriate user-generated...

Analytics & Experimentation
61
0
191 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
53 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Determine High-Quality Notifications with CTR Analysis

Determine High-Quality Notifications with CTR Analysis Push Notification Quality: Metric, Baseline Assessment, and Experiment Design Background A mobi...

Analytics & Experimentation
5
0
43 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Design Metrics to Track and Analyze Spam Impact

Design Metrics to Track and Analyze Spam Impact Scenario A messaging product team wants to reduce spam without harming normal user experience. You do ...

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

Evaluate Probability of Positive User Comments and Model Performance

Evaluate Probability of Positive User Comments and Model Performance Social-Media Positivity: Independence and Model Comparison Context You are evalua...

Statistics & Math
107
0
382 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data ScientistSenior+

Describe Overcoming Ambiguity and Building Cross-Team Collaboration

Describe Overcoming Ambiguity and Building Cross-Team Collaboration Behavioral & Leadership Interview — Senior Data Scientist (IC5) Context You are in...

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

Reflect on Conflict Resolution and Key Learnings

Reflect on Conflict Resolution and Key Learnings Behavioral Interview Prompts (Data Scientist, Onsite) Instructions Use the STAR framework (Situation,...

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

Analyze Key Metrics for Notification System Success

Analyze Key Metrics for Notification System Success Scenario You are evaluating a new push-notification system for a social app. The goal is to determ...

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

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively

Improve Team Dynamics: Addressing Unwelcoming Behavior Effectively Behavioral & Leadership (Meta, Data Scientist) — Onsite Scenario You are interviewi...

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

Convince Leadership to Launch Group Chat Feature

Convince Leadership to Launch Group Chat Feature Evaluating a Group Chat / Group Video-Call Feature for Instagram Context You are a Data Scientist ask...

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

Describe Facebook User Comment Distribution Shape and Justification

Describe Facebook User Comment Distribution Shape and Justification Characterizing Comments per User on Facebook Context You are analyzing the number ...

Statistics & Math
26
0
44 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze User-Comment Distribution to Understand Engagement

Analyze User-Comment Distribution to Understand Engagement Meta DSPA Analytics Exercise: Comment Engagement Distribution Context You have three canoni...

Analytics & Experimentation
43
0
159 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes

Analyze Algorithm's Impact on Diverse Demographics and Validate Causes A/B Test: Heterogeneous Lift in CTR for a New Ad-Ranking Algorithm Context You ...

Analytics & Experimentation
64
0
166 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Impact of Re-share Button on User Engagement

Determine Impact of Re-share Button on User Engagement Assessing Whether the Re-share Button Hurts Engagement Context The platform has a "Re-share" bu...

Analytics & Experimentation
5
0
49 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
47 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Probability of Video Selection and Impact Evaluation

Determine Probability of Video Selection and Impact Evaluation Video Recommendation Push: Selection Probabilities, Complements, and Design Choices Sce...

Statistics & Math
3
0
32 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
5
0
36 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Software Engineer

Implement, Debug, and Optimize a React Table

Implement, Debug, and Optimize a React Table React Table Component — Design, Implementation, and Performance Context: You are building a reusable Reac...

Software Engineering Fundamentals
7
0
60 people solved
Aug 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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