Meta Interview Questions

Meta 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
Machine Learning EngineerSenior+ AI Locked

Optimize a Fifteen-Sum Card Strategy

This question evaluates algorithmic problem-solving, debugging, simulation-based testing, and strategy optimization using techniques such as search, m...

Coding & Algorithms
6
0
58 people solved
Mar 1, 2026
Meta logo
Meta
Hard
Software Engineer

Check and infer custom alphabet

You are working with an unknown language that uses a custom alphabetical order, such as ['a', 'd', 'c', 'b', ...]. Implement the following: 1. Check s...

Coding & Algorithms
4
1
67 people solved
Feb 28, 2026
Meta logo
Meta
Medium
Data Scientist

Analyze spend cohort and source shifts

You work on an ads platform. Assume all timestamps are in UTC. Interpret last year as calendar year 2023 and this year as calendar year 2024. Tables: ...

Data Manipulation (SQL/Python)
11
2
80 people solved
Feb 23, 2026
Meta logo
Meta
Medium
Data Scientist Locked

Find least active countries

This question evaluates proficiency in SQL-based data manipulation and analytics, focusing on time-based filtering, distinct aggregation, grouping, th...

Data Manipulation (SQL/Python)
4
1
42 people solved
Feb 22, 2026
Meta logo
Meta
Medium
Software Engineer

Find Kth Largest and Tree Ancestors

In a technical phone interview, the candidate was asked two coding problems in the same round: 1. K-th largest element in an array Given an unsor...

Coding & Algorithms
3
0
56 people solved
Feb 22, 2026
Meta logo
Meta
Easy
Data Scientist Locked

Detect bots using comment distribution patterns

This question evaluates a candidate's competency in behavioral analytics, feature engineering, anomaly and bot detection, statistical validation, and ...

Analytics & Experimentation
4
0
58 people solved
Feb 16, 2026
Meta logo
Meta
Easy
Analytics Engineer Locked

Compute daily active ads

This question evaluates proficiency in data manipulation and analytics engineering, focusing on time-based event aggregation, status-based filtering, ...

Data Manipulation (SQL/Python)
1
0
30 people solved
Feb 15, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Extend BFS maze solver with keys and arrows

This question evaluates mastery of breadth-first search, state-space modeling for grid-based traversal, and handling augmented states like keys and on...

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

Maximize unique characters from a word list

This question evaluates algorithmic problem-solving skills focused on combinatorial subset selection and character-uniqueness constraints in strings, ...

Coding & Algorithms
11
0
84 people solved
Feb 12, 2026
Meta logo
Meta
Medium
Software EngineerSenior+ AI

Solve Matrix, Tree, Nested, LCA, Maze Tasks

Answer the following independent coding tasks. For each task, implement a clean API, handle edge cases, and analyze time and space complexity. 1. Trav...

Coding & Algorithms
0
0
7 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Machine Learning Engineer Locked

Maximize concatenation length with unique chars

This question evaluates understanding of string manipulation, set-based uniqueness constraints, and combinatorial subset selection for maximizing conc...

Coding & Algorithms
3
0
47 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design a live video comments system

This question evaluates skills in designing scalable, low-latency real-time systems, covering fan-out delivery, ordering and consistency semantics, du...

System Design
8
0
63 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software EngineerSenior+ Locked

Design a Top-K trending items service

This question evaluates competency in large-scale system design, real-time streaming ingestion, distributed aggregation, and trade-offs between exact ...

System Design
8
0
95 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer

Extend cloud file system with copy and compression

In-Memory Cloud File System V2: Copy, Capacity Updates, Compress/Decompress Design an in-memory file system with per-user quotas and additional operat...

Coding & Algorithms
9
0
71 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer

Design a bank system with scheduled transfers

Bank System (OOD): Accounts, Transfers, Scheduling, Top Spending, Merge Design an in-memory banking system that supports: - account creation - deposit...

Coding & Algorithms
2
0
41 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer

Design an in-memory cloud file system

In-Memory Cloud File System (OOD) Design an in-memory cloud file system that supports files owned by users with storage quotas. Data model / rules - E...

Coding & Algorithms
5
0
43 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Solve three array/string/graph coding tasks

This set of three coding tasks evaluates proficiency in fundamental data structures and algorithms, covering multi-way array merging, custom character...

Coding & Algorithms
4
0
59 people solved
Feb 11, 2026
Meta logo
Meta
Medium
Data Scientist Locked

Evaluate Notification-Based Account Ranking

This question evaluates a data scientist's competency in causal inference, A/B test and experiment design, metric definition and selection, statistica...

Analytics & Experimentation
3
0
22 people solved
Feb 9, 2026
Meta logo
Meta
Medium
Machine Learning Engineer AI Locked

Extend a Maze Solver

This question evaluates proficiency in implementing and debugging graph search algorithms and state-space modeling, including correct visited-state ha...

Coding & Algorithms
2
0
32 people solved
Feb 8, 2026
Meta logo
Meta
Easy
Data Scientist

Compute multi-account user distribution and unread pct

You are working on a product where a user can have multiple accounts, and each account can receive notifications. Tables Assume the following schemas:...

Data Manipulation (SQL/Python)
4
0
36 people solved
Feb 3, 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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