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
Easy
Machine Learning Engineer Locked

Solve two linked list/array tasks

This two-part question evaluates proficiency with fundamental data structures and algorithmic problem-solving—specifically linked list manipulation an...

Coding & Algorithms
3
0
63 people solved
Dec 14, 2025
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Meta
Medium
Data Scientist

Interpreting confidence intervals to choose a treatment

Feed-ranking Tweaks: Interpret Confidence Intervals and Choose a Treatment You ran online experiments for three feed-ranking tweaks. The primary metri...

Analytics & Experimentation
13
0
79 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Expected round of first selection in repeated sampling

Random Perk Selection: Expected First Round There are 1,000 employees. Each round, 10 distinct employees are selected at random for a perk. No one can...

Analytics & Experimentation
13
0
38 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Metrics for Group-Video Calling Experiment Success

Determine Metrics for Group-Video Calling Experiment Success You are the data scientist for a large consumer messaging app that currently supports one...

Analytics & Experimentation
84
0
294 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Design Experiment to Evaluate New Video-Ad Effectiveness

Design an Experiment to Evaluate New Video-Ad Effectiveness A large consumer app is considering a new video-ad format with changes to UI, creative ren...

Analytics & Experimentation
83
0
43 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Calculate Weekly CTR and Campaign-Specific CTR in SQL

AdEvents ad_id | campaign_id | event | view_id | event_date 1 | 10 | impression | 123 | 2023-11-07 1 | 10 | cli...

Data Manipulation (SQL/Python)
66
2
246 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Social Media's Brand Advertising Effectiveness

Evaluate Social Media's Brand Advertising Effectiveness A retailer runs both direct-response ads and brand-awareness ads. Leadership suspects social-m...

Analytics & Experimentation
77
0
192 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Identify Probability Distributions for Modeling Ad Clicks

Identify Probability Distributions for Modeling Ad Clicks You are interviewing for a data scientist role on an ads team. The interviewer asks you to d...

Statistics & Math
73
0
144 people solved
Jul 12, 2025
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Meta
Medium
Data ScientistNew Grad

Describe Overcoming a Major Challenge in Your Career

Describe Overcoming a Major Challenge in Your Career This is a behavioral deep-dive for a new-grad data scientist role. The interviewer may ask severa...

Behavioral & Leadership
94
0
238 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Model Unique Recipients with Poisson Distribution and Test Fit

Model Unique Recipients with a Count Distribution and Test Fit You need to model the distribution of the number of unique recipients each caller conta...

Statistics & Math
45
0
62 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist Locked

Identify Potential Users for Instagram Shopping Tab Adoption

Evaluates how to identify likely adopters of an Instagram Shopping tab and measure whether the feature creates incremental commerce value. Strong answ...

Analytics & Experimentation
34
0
93 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Design and Validate Initial Restaurant Recommendation Model

Design and Validate an Initial Restaurant Recommendation Model You are designing a first-iteration machine-learning model to recommend restaurants to ...

Machine Learning
29
0
112 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Facebook's Restaurant Recommendation Viability Using Data

Determine Facebook's Restaurant Recommendation Viability Using Data Facebook may launch a restaurant-recommendation product that helps people discover...

Analytics & Experimentation
6
0
30 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Determine Success Metrics for New Group Video-Call Feature

Determine Success Metrics for a New Group Video-Call Feature Meta is exploring a new group video-call feature. You need to estimate demand before laun...

Analytics & Experimentation
32
0
97 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Analyze Data to Boost Group Post Comment Rates

Analytics Plan to Increase Group Post Comment Coverage A social shopping platform wants to increase the percentage of group posts that receive at leas...

Analytics & Experimentation
69
0
178 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Determine Group Call Feature Need and Evaluation Methods

Determine Need and Evaluation Methods for Group Calling You are the product analyst for a messaging platform considering a group-calling feature. You ...

Analytics & Experimentation
94
0
260 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Define Metrics and Account for Network and Novelty Effects

Metrics for Notification-Triggered In-App Surveys Meta's notification system triggers optional in-app surveys to measure user sentiment after notifica...

Analytics & Experimentation
65
0
88 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Classify Reviewers Using Bayesian Probability for Accuracy Analysis

Classify Reviewers With Bayesian Probability You are auditing reviewers who may be lazy or careful. Each reviewer completes n gold-standard review tas...

Machine Learning
92
0
255 people solved
Jul 12, 2025
Meta logo
Meta
Easy
Data Scientist

Calculate Expected Impressions and Probability for Users

Expected Impressions From Random Ad Allocation There are X distinct users and Y ad impressions. Each impression is assigned independently and uniforml...

Statistics & Math
17
0
57 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Estimate Lift and Significance in Facebook Ad Campaigns

Estimate Lift and Significance in Facebook Ad Campaigns An advertiser is running campaigns on Facebook and wants to know whether ads increased convers...

Statistics & Math
12
0
81 people solved
Jul 12, 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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