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
Machine Learning Engineer

Design Place Recommendation System

Design a machine learning system for a maps or local-discovery product that recommends places a user may want to visit. The system should provide pers...

ML System Design
8
0
148 people solved
Mar 17, 2026
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Meta
Medium
Data Scientist

Compare Survey Satisfaction for New and Established Users

The interview report preserved the survey tables and the request to compare response levels for new and old users, but it explicitly noted that the in...

Data Manipulation (SQL/Python)
3
0
66 people solved
Jul 6, 2026
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Meta
Medium
Software Engineer AI

Design a Code Review Agent

Design an AI-powered code review agent that assists developers by reviewing pull requests and producing actionable feedback. The agent should be able ...

ML System Design
7
0
63 people solved
Mar 17, 2026
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Meta
Medium
Data Scientist

Measure scheduled posts feature success

Facebook is considering launching a new feature that allows users to schedule a post to be published at a future time. The product hypothesis is that ...

Analytics & Experimentation
12
0
136 people solved
Apr 30, 2026
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Meta
Medium
Software Engineer Locked

Validate Sorted Order Under a Custom Alphabet

This question evaluates a candidate's ability to apply custom ordering rules and string comparison logic, a common variant of array and string validat...

Coding & Algorithms
2
0
17 people solved
Jun 20, 2026
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Meta
Medium
Software EngineerSenior+ Locked

Design Search And Web Crawling Systems

This question evaluates system-design and distributed-systems competencies including large-scale indexing and query-serving, relevance ranking and per...

System Design
6
0
66 people solved
May 21, 2026
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Meta
Medium
Software Engineer Locked

Find Shortest Unique Prefixes

This question evaluates string-processing skills and understanding of data structures for prefix management (e.g., tries), along with algorithmic effi...

Coding & Algorithms
1
0
13 people solved
Jun 18, 2026
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Meta
Medium
Data Scientist

How should you evaluate unconnected content?

A social media platform has launched a feed feature that increases the share of unconnected content, meaning posts from creators who do not have an ex...

Analytics & Experimentation
13
0
139 people solved
Apr 5, 2026
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Meta
Easy
Data Scientist

How to evaluate a similar-listing notifications feature

Question You are a Data Scientist on a US C2C marketplace app (like Facebook Marketplace) where users buy and sell second-hand products. Current produ...

Analytics & Experimentation
92
1
776 people solved
Jan 17, 2026
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Meta
Medium
Machine Learning Engineer Locked

Prevent Private Code Leakage in Coding Agents

This question evaluates competency in ML system design, data privacy and security, model training and inference safeguards, and mechanisms for detecti...

ML System Design
6
0
102 people solved
Apr 9, 2026
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Meta
Easy
Data Scientist Locked

How would you evaluate emoji reactions launch?

This question evaluates a data scientist's competency in analytics and experimentation, covering metric framework design, A/B testing and quasi-experi...

Analytics & Experimentation
40
0
482 people solved
Feb 21, 2026
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Meta
Medium
Software Engineer Locked

Explain Collaboration, Ambiguity, and Prioritization

This question evaluates collaboration, ambiguity management, and prioritization competencies, focusing on interpersonal communication, stakeholder ali...

Behavioral & Leadership
3
0
51 people solved
May 14, 2026
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Meta
Medium
Data Scientist

Analyze Thirty-Day Ad Performance with SQL

Analyze Thirty-Day Ad Performance with SQL For this practice version, use the following neutral schema. clicked is a Boolean recorded on each impressi...

Data Manipulation (SQL/Python)
1
0
23 people solved
May 22, 2026
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Meta
Medium
Data Scientist

Estimate ads ranking revenue impact

You are the data scientist for an ads ranking team at a large social platform. The team has built a new ranking algorithm for feed ads. The new model ...

Analytics & Experimentation
54
0
365 people solved
Apr 30, 2026
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Meta
Medium
Product AnalystSenior+

Describe an Analysis Where You Used AI Responsibly

Prompt Describe one analysis in which you used an AI-assisted tool. Explain the business question, why AI was appropriate, exactly what the tool did, ...

Behavioral & Leadership
1
0
26 people solved
Apr 20, 2026
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Meta
Medium
Machine Learning EngineerSenior+

Answer senior-level behavioral interview questions

You are interviewing for a senior machine-learning engineer role on the tech-lead track at Meta, targeting roughly the IC6+ level. This is the first-r...

Behavioral & Leadership
20
0
174 people solved
Jan 28, 2026
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Meta
Medium
Software EngineerSenior+ AI

Solve Three Coding Interview Problems

The interview included three coding tasks: 1. Common ancestor in an N-ary tree You are given the root of an N-ary tree and two target nodes. Dete...

Coding & Algorithms
4
0
50 people solved
Feb 2, 2026
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Meta
Medium
Data Scientist Locked

Calculate CTR and ad revenue

This question evaluates proficiency in data manipulation and analytics, specifically metric calculation (CTR) and multi-currency revenue aggregation, ...

Data Manipulation (SQL/Python)
7
1
72 people solved
Jan 25, 2026
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Meta
Medium
Software EngineerSenior+ AI

Design an Online Judge and Live Comments

The onsite included two system design prompts: 1. Design an online judge platform where users submit code for programming problems. The system must su...

System Design
24
0
170 people solved
Apr 8, 2026
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Meta
Easy
Software EngineerSenior+

Design a location-based radius top-K search

Design a location-based search service. Input: - latitude, longitude - radius (meters) - K Output: - The top K locations within the radius. The term “...

System Design
33
0
291 people solved
Mar 11, 2026

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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