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
Hard
Data Scientist

Design an A/B test for WhatsApp call reliability

A/B Test Design: Adaptive Codec for Unstable Networks (WhatsApp Calling) Context You join the Calling organization. A PM proposes enabling an adaptive...

Analytics & Experimentation
5
0
54 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design a small-sample launch experiment in Europe

Launch Test Design: Early-Access EU Businesses Context You have an early-access pool of 1,200 EU businesses for a new chat subscription offering. Chat...

Analytics & Experimentation
3
0
27 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Define success metrics and guardrails for B2B chat

Define a Success-Measurement Plan for a New EU B2C Chat Subscription You are launching a paid business-to-customer chat subscription in the EU. Design...

Analytics & Experimentation
7
0
66 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Choose robust metrics for skewed comments

Robust central tendency and inference for zero‑inflated, heavy‑tailed counts You are evaluating an A/B test on per‑user daily comment counts. The outc...

Statistics & Math
10
2
74 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze regression to mean in heavy-tailed shares

Cohort Dynamics of a Right-Skewed Daily Shares Metric Context - You have a right-skewed metric: daily shares per user, with a long tail. - On Day 1, y...

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

Derive expected meetings given nonempty room

Zero-Truncated Binomial: Random Room Assignment Setup - There are N rooms labeled 1, 2, ..., N. - K meetings are scheduled; each meeting independently...

Statistics & Math
3
0
30 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Compare two ad insertion strategies

Ad Insertion Strategies for a 100-Post Feed You are evaluating two ad-insertion strategies on a feed with 100 posts: - Strategy A (Stochastic): Indepe...

Analytics & Experimentation
1
0
30 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Model user-level ad impression allocation

Random Assignment of Ad Impressions to Users Context - There are X distinct users and Y ad impressions (X ≥ 1, Y ≥ 0 integers). - Each impression is i...

Statistics & Math
7
0
74 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Handle sales pressure with analytical integrity

Interview Scenario: Call Volume vs. Win Rate — Causation vs. Correlation You support Sales as a data scientist. Leadership observed a positive correla...

Behavioral & Leadership
3
0
24 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Choose group-call participant cap via distribution

Group Call Cap Decision: QoS vs Reach You are deciding whether to cap the maximum number of participants in a group call. You have the past 28-day dis...

Statistics & Math
6
0
44 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design A/B test and success metrics for new feature

Instagram Collections 2.0 — Define Success, Experiment Design, and Measurement Context: You are proposing a new Instagram feature, Shareable Collectio...

Analytics & Experimentation
7
0
52 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Compute feed ad frequency and retention in SQL

Assume today is 2025-09-01. Schema and tiny samples: feed_impressions(impression_id, user_id, impression_time, content_type, feed_position, session_id...

Data Manipulation (SQL/Python)
7
0
58 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Communicate trade-offs and influence launch

Product Experiment Trade‑off: Notifications for Multi‑Account Users Context You ran an experiment on notification delivery to users who often maintain...

Behavioral & Leadership
1
0
29 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design Messenger spam experiment with clustering

Experiment Design: Spam-Detection Algorithm for Messenger You are evaluating a new spam-detection algorithm that routes suspected spam into a separate...

Analytics & Experimentation
3
0
42 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design metrics for violating content exposure

Measuring User Exposure to Violating Content on a UGC Platform Context You work on a large-scale user-generated content (UGC) platform that uses autom...

Analytics & Experimentation
2
0
26 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Software Engineer Locked

Design an online coding contest platform

This question evaluates a candidate's ability to design scalable, reliable, and secure distributed systems for real-time, event-driven workloads, cove...

System Design
4
0
64 people solved
Feb 7, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Solve maze reachability and two follow-ups

This set of tasks evaluates graph and state-space search with constrained movement (maze rolling ball), tree algorithms and recursion for computing di...

Coding & Algorithms
12
0
96 people solved
Feb 7, 2026
Meta logo
Meta
Easy
Data Scientist Locked

Design an ad recommendation ranking approach

This question evaluates competency in machine-learning driven ad ranking and recommendation systems, including objective formulation, modeling strateg...

Machine Learning
8
0
61 people solved
Dec 6, 2025
Meta logo
Meta
Easy
Data Scientist

Define engagement metrics and analyze comment distribution

You are a Data Scientist for a video platform. A PM asks you to: 1) Define metrics for “engagement” (they want a clear metric framework they can use i...

Analytics & Experimentation
11
0
87 people solved
Dec 6, 2025
Meta logo
Meta
Medium
Product Manager

Children’s Bookshelf Product Design

Product Design Prompt: Children's Bookshelf Design a primarily physical bookshelf for children to use at home, with optional lightweight digital suppo...

Product / Decision Making
12
0
44 people solved
Jul 4, 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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