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
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Meta
Easy
Data Scientist Locked

How would you evaluate pixel-issue notifications?

This question evaluates a data scientist's skills in experimentation design, metric framework development, causal inference, and measurement-aware ana...

Analytics & Experimentation
10
0
98 people solved
Feb 18, 2026
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Meta
Easy
Data Scientist Locked

Convert multi-currency revenue to USD totals

This question evaluates a candidate's competency in converting multi-currency revenue into USD totals by aligning event dates with FX rates, handling ...

Data Manipulation (SQL/Python)
4
0
47 people solved
Feb 16, 2026
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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
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Meta
Medium
Data Scientist Locked

Evaluate a new-listing notification feature

This question evaluates product analytics, causal inference, experimentation design, metric definition, and business-impact estimation competencies fo...

Analytics & Experimentation
3
0
28 people solved
Feb 15, 2026
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Meta
Medium
Software Engineer Locked

Determine feasibility and clean parentheses string

This pair of problems evaluates proficiency with graph algorithms (dependency modeling and cycle detection/topological ordering) and string processing...

Coding & Algorithms
29
0
195 people solved
Feb 12, 2026
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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
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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
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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
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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
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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
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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
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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
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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
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Meta
Medium
Software Engineer

Solve tree view and parentheses cleanup

Solve the following two algorithm problems. 1. Right-side view of a binary tree Given the root of a binary tree, return the values of the nodes that w...

Coding & Algorithms
6
0
43 people solved
Feb 1, 2026
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Meta
Hard
Machine Learning Engineer

Solve Three Algorithmic Problems

Three coding questions were mentioned: 1. Second-largest distinct permutation Given an array of integers that may contain duplicates, consider all ...

Coding & Algorithms
5
0
40 people solved
Jan 30, 2026
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Meta
Easy
Software Engineer Locked

Compute Sliding Window Averages

This question evaluates proficiency in array manipulation, sliding-window techniques, and algorithmic efficiency including time and space complexity. ...

Coding & Algorithms
1
0
20 people solved
Jan 29, 2026
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Meta
Easy
Software Engineer Locked

Solve four OA coding problems

This set of four problems evaluates core algorithmic competencies including array manipulation and counting in sorted arrays, resource-constrained opt...

Coding & Algorithms
2
0
50 people solved
Jan 26, 2026
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Meta
Medium
Machine Learning EngineerSenior+ Locked

Find two numbers summing to target without hashmap

This question evaluates array manipulation, index-tracking, and algorithmic problem-solving skills, with emphasis on time and space complexity trade-o...

Coding & Algorithms
4
0
73 people solved
Jan 22, 2026
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Meta
Medium
Software EngineerSenior+ Locked

Solve string transform and min-move sorting

This two-part problem evaluates string manipulation and algorithmic problem-solving competencies, specifically rule-based text transformation and comp...

Coding & Algorithms
6
0
99 people solved
Jan 22, 2026
Meta logo
Meta
Medium
Data Scientist

Assess ranking change and design experiment

A multi-account product currently orders a user's accounts by most recent visit. The product team wants to change the ranking so that accounts with th...

Analytics & Experimentation
3
0
46 people solved
Jan 21, 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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