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
Meta logo
Meta
Hard
Product Manager

Prioritize voice-to-text or assistant?

For the same accessibility-focused VR product, assume the team can ship only one feature first because engineering capacity is limited: voice-to-text ...

Product / Decision Making
4
0
38 people solved
Apr 10, 2024
Meta logo
Meta
Hard
Product Manager

Should Meta build accessible VR?

You are a Product Manager in Meta's New Product Introduction organization for VR hardware. The team is considering an accessibility-focused VR experie...

Product Design & Strategy
3
0
42 people solved
Apr 10, 2024
Meta logo
Meta
Medium
Software Engineer

Sum numbers formed by root-to-leaf paths

You are given the root of a binary tree where each node contains a single digit from 0 to 9. Each root-to-leaf path represents a number obtained by co...

Coding & Algorithms
4
0
39 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Design ticketing system with seat hold

Design an online ticketing system (similar to Ticketmaster) that allows users to browse events, select seats, and place a temporary 5-minute hold on s...

System Design
4
0
59 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Explain your main growth area

What is one of your main growth or development areas right now? Explain: - What specific skill or behavior you are working to improve. - How you ident...

Behavioral & Leadership
4
0
81 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Merge overlapping intervals

You are given a list of closed intervals on the number line, where each interval is represented as a pair of integers [start, end] with start <= end. ...

Coding & Algorithms
4
0
48 people solved
Dec 8, 2025
Meta logo
Meta
Hard
Product Manager

Product Metrics & Debugging Scenarios

Product Metrics and Debugging Scenarios You are a PM candidate evaluating data, metrics, and operational plans for large-scale consumer products. Answ...

Product / Decision Making
11
0
53 people solved
Jul 4, 2025
Meta logo
Meta
Medium
Product Manager

Meta Pay: Metrics & Prioritization

Product Metrics and Prioritization Prompt: Meta Pay Assume you are the PM for Meta Pay, the consumer payments system used across Meta apps such as Mes...

Product / Decision Making
15
0
58 people solved
Jul 4, 2025
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
94 people solved
Dec 8, 2025
Meta logo
Meta
Easy
Data Scientist

Compute percent of active users with 50+ calls

Problem You work on a Messenger-like app. You want to measure how many active users in Great Britain (GB) today have been heavy callers recently. Tabl...

Data Manipulation (SQL/Python)
8
1
110 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Implement string and basic ML algorithms

You are given three implementation tasks that mix algorithms and basic machine learning models. --- Task 1: Longest substring with all distinct charac...

Coding & Algorithms
5
0
65 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Prepare answers for core behavioral questions

Behavioral Questions Set You are preparing for a behavioral interview. Be ready to answer the following questions clearly and concretely, ideally usin...

Behavioral & Leadership
3
0
33 people solved
Dec 7, 2025
Meta logo
Meta
Medium
Product Manager

Apply GenAI to Business Messaging

Product Strategy Prompt: Apply GenAI to Enterprise Business Messaging Explain what generative AI is and how you would apply it to an enterprise busine...

Product Design & Strategy
3
0
50 people solved
Jul 1, 2025
Meta logo
Meta
Easy
Data Scientist

Describe a challenging project and work-style conflicts

Question This is the Meta Data Scientist onsite behavioral & leadership round. The anchor prompt is to describe your most challenging recent project e...

Behavioral & Leadership
16
0
101 people solved
Dec 6, 2025
Meta logo
Meta
Hard
Data Engineer

Evaluate impact of short videos in feed

Scenario You work on a social app’s main News Feed. The team wants to introduce a short-form video module ("Reels") into the feed. Prompt 1. How would...

System Design
18
0
166 people solved
Dec 1, 2025
Meta logo
Meta
Easy
Software Engineer

Implement list cloning and k-frequency finder

You are given two separate coding tasks. --- Problem 1: Deep copy a linked list with extra pointers You are given the head of a singly linked list. Ea...

Coding & Algorithms
3
0
73 people solved
Nov 27, 2025
Meta logo
Meta
Medium
Data Scientist

How would you predict a car’s turning intention?

At an intersection, there are n vehicles stopped or approaching. For each vehicle, you have a short history (e.g., last 3–10 seconds at 10 Hz) of: - P...

Machine Learning
7
0
54 people solved
Nov 24, 2025
Meta logo
Meta
Medium
Software Engineer Locked

Design an online chess game with undo and leaderboard

This question evaluates proficiency in real-time system design, state management, and concurrency control for multiplayer games, including consistency...

System Design
9
0
63 people solved
Nov 22, 2025
Meta logo
Meta
Easy
Data Scientist

Describe resolving conflict and welcoming others

Answer the following behavioral questions with specific examples: 1. How do you make other people feel welcome or included on a team? - Especially ...

Behavioral & Leadership
3
0
34 people solved
Nov 16, 2025
Meta logo
Meta
Easy
Data Scientist Locked

Define hand-waving accuracy and launch decision

This question evaluates a data scientist's ability to define and operationalize detection metrics, design instrumentation and diagnostics, connect mod...

Analytics & Experimentation
4
0
53 people solved
Nov 16, 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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