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
Software Engineer

Schedule and cancel delayed payments

Question Extend an existing in-memory payment system (immediate transfers between accounts, plus a top-N spenders/payers leaderboard) with scheduled p...

System Design
11
0
100 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Software Engineer

Walk through resume and plan team success

Behavioral/Leadership Prompt — Technical Screen (Software Engineer) You are interviewing for a Software Engineer role in a technical screen focused on...

Behavioral & Leadership
4
0
38 people solved
Sep 6, 2025
Meta logo
Meta
Medium
Software Engineer

Answer four string/array/battery coding questions

You are given four independent coding questions. Problem A — Uppercase vs lowercase count difference Given a string s (ASCII), count how many characte...

Coding & Algorithms
4
0
46 people solved
Dec 16, 2025
Meta logo
Meta
Medium
Machine Learning Engineer Locked

Debug and optimize a card-drawing strategy

This question evaluates debugging and implementation skills, combinatorial search and optimization, and the ability to design and interpret simulation...

Coding & Algorithms
21
0
153 people solved
Feb 12, 2026
Meta logo
Meta
Easy
Machine Learning Engineer Locked

Solve four OA string/array/matrix/graph tasks

This multi-part prompt evaluates proficiency in core programming competencies: string parsing and character classification, simulation/greedy processi...

Coding & Algorithms
10
0
77 people solved
Feb 12, 2026
Meta logo
Meta
Medium
Software Engineer

Design structure for top-K frequent elements

You are working with a large collection of items represented by integer IDs (e.g., product IDs, user IDs, etc.). Updates and queries arrive over time....

Coding & Algorithms
3
0
50 people solved
Oct 21, 2025
Meta logo
Meta
Hard
Software Engineer

Answer key behavioral prompts effectively

Prepare responses for these behavioral prompts: 1. Proudest project: Describe the project you’re most proud of and why. 2. Conflict: Tell me about a t...

Behavioral & Leadership
2
0
23 people solved
Oct 21, 2025
Meta logo
Meta
Medium
Software EngineerSenior+ Locked

Solve maze tasks and compute shortest routes

This multi-part question evaluates proficiency in grid-based pathfinding and shortest-path reasoning, debugging and incremental code extension, handli...

Coding & Algorithms
6
0
81 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Data Scientist

How to Validate Friends' Content Engagement Hypothesis?

Validate Friends' Content Engagement Hypothesis A Meta product team wants to know whether content from a viewer's friends or connected authors drives ...

Analytics & Experimentation
101
0
202 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Machine Learning Engineer Locked

Design recommendation and weapon-ad detection systems

This question evaluates proficiency in end-to-end ML system design, covering scalable recommendation systems and safety-focused ad classification with...

ML System Design
8
0
81 people solved
Dec 9, 2025
Meta logo
Meta
Medium
Software Engineer

Explain ACID and isolation levels

Explain what a database transaction is, define the ACID properties (Atomicity, Consistency, Isolation, Durability), and describe common transaction is...

Software Engineering Fundamentals
7
0
69 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Machine Learning Engineer AI Locked

Build Friend Recommendations

This question evaluates proficiency with graph data structures, set operations, uniform random sampling, counting mutual connections, and deterministi...

Coding & Algorithms
4
0
44 people solved
Feb 8, 2026
Meta logo
Meta
Easy
Data Scientist

Design and evaluate a new group call feature

Product / DS Case: Group Calls for Messenger Groups Messenger has Groups but does not currently support group calls. You are evaluating whether to bui...

Analytics & Experimentation
11
0
93 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Data Scientist Locked

Estimate bots and CI from DAU spike

This question evaluates proficiency in mixture modeling for anomaly detection, parametric and nonparametric inference for mean differences, handling o...

Statistics & Math
9
1
96 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Design analysis to test social vs game engagement

Question Hypothesis: Among Oculus (Meta Quest) users, those who use social features are more regularly engaged than those who use game features. Using...

Analytics & Experimentation
6
1
64 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Size opportunity for new product line

An e-commerce site considers adding a "Home Office" product line. Before any A/B test, size the opportunity and recommend whether to proceed. Assumpti...

Analytics & Experimentation
6
0
53 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design and critique teen-parent impact experiment

Causal Impact of Parental Registration on Teen Outcomes Meta plans to let parents register and link to their teen’s account. Leaders are concerned abo...

Analytics & Experimentation
3
0
34 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Run a clean A/B test for recommendations

You must run an A/B test to evaluate the new hashtag recommender starting on 2025‑09‑01. 1) Define the randomization unit (user/session/impression) an...

Analytics & Experimentation
3
0
39 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Design and analyze A/B test with interference

You must ship a News Feed ranking change where content produced by treated users can be seen by control users, creating interference and within-user c...

Analytics & Experimentation
3
0
52 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Resolve a team conflict decisively

Tell me about a time you resolved a significant conflict within a team under time pressure. Include: 1) the root causes (interests, incentives, commun...

Behavioral & Leadership
3
0
27 people solved
Oct 13, 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.

Explore more Meta interview questions

Jump straight to Meta questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Meta interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Software Engineer
Meta interview
Read the guide
Editorial prep
Data Scientist
Meta interview
Read the guide