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
Medium
Software Engineer

Remove runs of length at least k

Given a string s and an integer k (k >= 2), repeatedly remove every maximal contiguous group of the same character whose length is at least k, deletin...

Coding & Algorithms
2
0
24 people solved
Aug 12, 2025
Meta logo
Meta
Hard
Software Engineer

Implement held transfers with accept/cancel

Implement held transfers with accept/cancel Design and implement a two-phase ("pull-style") money-transfer system in which funds are first held on the...

System Design
6
0
42 people solved
Aug 9, 2025
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Meta
Medium
Software Engineer

Maintain top N payers

Maintain top N payers Implement topNPayers(n) that returns the n accounts with the highest cumulative outgoing payment amounts as strings formatted "{...

Coding & Algorithms
1
0
25 people solved
Aug 9, 2025
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Meta
Medium
Software Engineer

Group a tree by vertical columns

Group a tree by vertical columns Given the root of a binary tree, produce a vertical listing of nodes: assign each node an x-coordinate (root at x=0; ...

Coding & Algorithms
1
0
30 people solved
Aug 7, 2025
Meta logo
Meta
Medium
Software Engineer

Compute binary tree diameter

Compute binary tree diameter Given the root of a binary tree, compute the tree's diameter defined as the maximum number of edges on any path between t...

Coding & Algorithms
4
0
44 people solved
Aug 7, 2025
Meta logo
Meta
Medium
Software Engineer

Compute weighted sum of nested lists

Compute weighted sum of nested lists Given a nested collection that can contain integers or other nested collections, compute the depth-weighted sum w...

Coding & Algorithms
7
0
51 people solved
Aug 7, 2025
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Meta
Medium
Software Engineer

Solve UTF-8 Validation & Shortest Path

Question LeetCode 393. UTF-8 Validation LeetCode 1091. Shortest Path in Binary Matrix https://leetcode.com/problems/utf-8-validation/description/ http...

Coding & Algorithms
2
0
37 people solved
Aug 4, 2025
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Meta
Medium
Software Engineer

Solve weighted pick and product except self

Question LeetCode 528. Random Pick with Weight LeetCode 238. Product of Array Except Self https://leetcode.com/problems/random-pick-with-weight/descri...

Coding & Algorithms
5
0
36 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Demonstrate behavioral competencies

Demonstrate behavioral competencies Behavioral Interview Prompt: Prepare STAR Stories Context You are preparing for an onsite Behavioral & Leadership ...

Behavioral & Leadership
4
0
39 people solved
Aug 4, 2025
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Meta
Medium
Data Engineer

Aggregate Netflix metrics in SQL

Question Netflix video-streaming analytics SQL: Write a simple aggregation (e.g., total watch-time per day). Build a cumulative metric: today’s metric...

Data Manipulation (SQL/Python)
1
1
7 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Engineer

Validate carpool capacity

Question LeetCode 1094. Car Pooling – Given trips[i] = [numPassengers, start, end] and an integer capacity, return true if the vehicle can fulfill all...

Coding & Algorithms
5
0
68 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Software Engineer

Implement paginated, sortable dynamic table component

Implement paginated, sortable dynamic table component React Table Component: Pagination, Sorting, Dynamic Columns Context You are building a reusable ...

System Design
2
0
46 people solved
Aug 4, 2025
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Meta
Medium
Machine Learning Engineer

Check diagonal and compute window statistics

Question Given a square matrix, determine whether all elements on its main diagonal are identical. LeetCode 346. Moving Average from Data Stream – des...

Coding & Algorithms
9
0
69 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Calculate Response Rate and Compare New vs. Existing User Scores

survey_events +---------+------------+-----------+--------------+---------------------+ | user_id | is_new_user| responded | survey_score | event_time...

Data Manipulation (SQL/Python)
1
0
5 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Identify 3-Person Call Cycles in Video-Calling App

Calls callerid | recipientid | ds | call_id | duration 1001 | 2001 | 2023-02-20| 555 | 180 2001 | 3001 | 2023-02-20| ...

Data Manipulation (SQL/Python)
0
0
6 people solved
Aug 4, 2025
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Meta
Medium
Data Scientist

Compare Instagram and Facebook Stories Using Key Performance Metrics

Compare Instagram and Facebook Stories Using Key Performance Metrics Scenario You are a data scientist tasked with quantitatively comparing the succes...

Analytics & Experimentation
2
0
41 people solved
Aug 4, 2025
Meta logo
Meta
Hard
Data Scientist

Choose Randomization Unit and Mitigate Network Effects

Choose Randomization Unit and Mitigate Network Effects A/B Test Design for a New Messenger Feature with Network Effects Context You are designing an A...

Statistics & Math
4
0
37 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Launch Sticker-Reply Feature in Facebook Groups?

Launch Sticker-Reply Feature in Facebook Groups? Launch Decision: Sticker-Reply Feature for Facebook Groups Context You are evaluating whether to laun...

Analytics & Experimentation
3
0
24 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Overcome Challenges and Build Trust in Teamwork

Overcome Challenges and Build Trust in Teamwork Behavioral Interview: Teamwork, Feedback, Trust, and Conflict (Data Scientist) Context You are intervi...

Behavioral & Leadership
2
0
44 people solved
Aug 4, 2025
Meta logo
Meta
Medium
Data Scientist

Retrieve Ad Metrics and Rates for Last 7 Days

ads +--------+---------------+----------+ | ad_id | advertiser_id | ad_type | +--------+---------------+----------+ | 101 | 1001 | image...

Data Manipulation (SQL/Python)
0
0
9 people solved
Aug 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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