Meta Interview Questions

Meta Analytics & Experimentation 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
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Meta
Medium
Data Scientist

Pivot Projects Quickly and Foster Team Inclusion

Pivot Projects Quickly and Foster Team Inclusion Meta Data Scientist Onsite — Behavioral & Leadership (STAR) Scenario You’ve joined a cross‑functional...

Behavioral & Leadership
27
0
80 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
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Meta
Medium
Data Scientist

Promote Inclusion and Overcome Barriers in Social-Commerce Team

Promote Inclusion and Overcome Barriers in Social-Commerce Team Behavioral Interview: Barriers, Feedback, and Inclusion (Data Scientist — Social Comme...

Behavioral & Leadership
26
0
93 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Evaluate Classifier with Precision, Recall, and Fairness Metrics

Evaluate Classifier with Precision, Recall, and Fairness Metrics Offline Evaluation Framework for a Harmful-Content Video Classifier Context You are e...

Machine Learning
4
0
44 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Analyze Video View Distribution: Mode, Median, Mean Comparison

Analyze Video View Distribution: Mode, Median, Mean Comparison Scenario You are analyzing user engagement on a short-video sharing product. The team n...

Statistics & Math
65
0
142 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
35 people solved
Aug 4, 2025
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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
90 people solved
Aug 4, 2025
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Meta
Hard
Data Scientist

Design an Experiment to Evaluate New ML Model

Design an Experiment to Evaluate New ML Model Experiment Design: Validating a New Ads Ranking Model Context You operate an ads platform with an existi...

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

Evaluate Success of Group Video Feature with Key Metrics

Evaluate Success of Group Video Feature with Key Metrics Evaluate the Success of a New Group Video Feature Context You are assessing the launch of a G...

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

Calculate Expected Comments and Confidence Interval Analysis

Calculate Expected Comments and Confidence Interval Analysis Scenario You are analyzing the distribution of comment counts per post for a social platf...

Statistics & Math
3
0
25 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Solve PTO window and sparse unique counting

Solve PTO window and sparse unique counting Design algorithms for two problems: A) Longest vacation with limited PTO: Given an array A of characters w...

Coding & Algorithms
2
0
38 people solved
Aug 1, 2025
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Meta
Medium
Software Engineer

Design keyword search for social posts

Design a Facebook-like post search feature: - Input: one or more keywords. - Output: all (or top-K) posts that contain the keyword(s). - Assume posts ...

System Design
4
0
30 people solved
Oct 2, 2025
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Meta
Hard
Software EngineerSenior+

Describe conflict resolution and mentoring experiences

You are interviewing for an engineering manager or senior engineer role. Prepare structured behavioral answers for these four prompts: 1. Describe a s...

Behavioral & Leadership
4
0
37 people solved
Mar 23, 2025
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Meta
Medium
Product Analyst Locked

Write SQL for call metrics

This question evaluates a candidate's competency in SQL-based data manipulation and analytics, specifically aggregations, JOINs across relational tabl...

Data Manipulation (SQL/Python)
4
0
47 people solved
Mar 19, 2026
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Meta
Medium
Machine Learning Engineer Locked

Design comment ranking for a news feed

This question evaluates a candidate's ability to design an ML-powered comment-ranking system, testing competencies in personalization, engagement and ...

ML System Design
6
0
99 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer Locked

Design a Netflix-like video streaming service

This question evaluates competency in designing large-scale streaming platforms, focusing on distributed systems, content storage and encoding pipelin...

System Design
4
0
49 people solved
Dec 15, 2025
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Meta
Medium
Machine Learning Engineer

Compute nested depth sum and grid distance

Problem A: Weighted sum of integers in a nested list You are given a nested list structure that may contain integers or other nested lists. Define the...

Coding & Algorithms
9
0
68 people solved
Dec 15, 2025
Meta logo
Meta
Hard
Software Engineer

Design place-of-interest ML system

Design place-of-interest ML system Design a POI (Places of Interest) Recommendation System Context Design a global POI recommender for a mobile maps/f...

ML System Design
13
1
117 people solved
Jul 17, 2025
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Meta
Medium
Software Engineer AI

Maximize Unique Characters from Words

You are given a list of lowercase English words. Select a subset of the words such that no character appears more than once across all selected words....

Coding & Algorithms
1
0
13 people solved
Mar 17, 2026
Meta logo
Meta
Hard
Software Engineer

Design a Dropbox-like file storage and sync service

Design a Dropbox-like file storage and sync service Design a cloud file storage and synchronization service like Dropbox / Google Drive. The system sh...

System Design
6
0
54 people solved
Jul 15, 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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