Meta Interview Questions

Meta 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

Identify Probability of Request Originating from Bad User

Identify Probability of Request Originating from Bad User Measuring Abuse in Friend-Requests: Bayes, Identification, and Precision Scenario A social-n...

Statistics & Math
5
0
35 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Determine User Demand for New Video-Calling Feature

Determine User Demand for New Video-Calling Feature Analytics Design: Demand Sizing, Evaluation Metrics, and Trade-offs for a New Video-Calling Featur...

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

Determine Success Metrics for Instagram Video-Call Feature

Determine Success Metrics for Instagram Video-Call Feature Instagram Group Video-Call MVP: Defining Success and Metrics Context You are the data scien...

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

Detect and Reduce Spammy Friend Requests Effectively

Detect and Reduce Spammy Friend Requests Effectively Detecting Spammy Friend Requests Context Assume a consumer social platform where users can send f...

Machine Learning
2
0
26 people solved
Aug 4, 2025
Meta logo
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
11 people solved
Aug 4, 2025
Meta logo
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
28 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Boost Engagement and Purchases in Meta Social Products

Boost Engagement and Purchases in Meta Social Products Meta Social Products: Driving Comments in Facebook Groups and In‑App Purchases on Instagram Con...

Analytics & Experimentation
2
0
30 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
2
0
43 people solved
Aug 4, 2025
Meta logo
Meta
Easy
Data Scientist

Determine Significance of Model B's Performance Improvement

Determine Significance of Model B's Performance Improvement A/B Test: Two-Proportion Z-Test for Success Rates Scenario You ran an A/B test comparing t...

Analytics & Experimentation
3
0
28 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Chatbot's Retailer Value and Launch Viability

Evaluate Chatbot's Retailer Value and Launch Viability Scenario You are evaluating whether to launch a B2C chatbot for retailers on a commerce messagi...

Analytics & Experimentation
2
0
45 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

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
46 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate Fake Accounts Using Data Signals and Sampling

Estimate Fake Accounts Using Data Signals and Sampling Estimating Fake Accounts on a Social Network Background A large social platform wants to estima...

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

Optimize Travel Costs and Generate Rotational Symmetric Numbers

Scenario You are building a travel-search engine that must 1) show customers the cheapest round-trip they can book if departure and return prices vary...

Coding & Algorithms
8
0
59 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Instagram Shopping Tab Success with Key Metrics

Evaluate Instagram Shopping Tab Success with Key Metrics Instagram Shopping Tab: Post-Launch Evaluation and Sizing Context You are evaluating the succ...

Analytics & Experimentation
2
0
35 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Design Sampling Strategy to Estimate Fake News Proportion

Design Sampling Strategy to Estimate Fake News Proportion Estimating Fake News Prevalence and Impact on Facebook Context Management is concerned about...

Statistics & Math
24
0
31 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
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
83 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Explain Algorithm's Disproportionate Impact on Demographic Segments

Explain Algorithm's Disproportionate Impact on Demographic Segments Ad-Ranking A/B Test: Interpreting Heterogeneous CTR Lifts Context You ran a standa...

Analytics & Experimentation
75
0
248 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data ScientistSenior+ Locked

Prove high-quality pixels improve ad performance

Prove high-quality pixels improve ad performance evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and...

Analytics & Experimentation
1
0
29 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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