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
Medium
Software Engineer Locked

Compute dot product of sparse vectors

This question evaluates proficiency with sparse data representations and algorithmic efficiency by requiring the dot product to be computed from lists...

Coding & Algorithms
3
0
33 people solved
Jan 18, 2026
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Meta
Easy
Data Scientist

Compute ads revenue by geography in SQL

You have ad delivery logs for a shop-ads system. Tables ad_impressions - impression_id STRING (PK) - ts TIMESTAMP (UTC) - user_id STRING - shop_id STR...

Data Manipulation (SQL/Python)
29
5
208 people solved
Jan 17, 2026
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Meta
Medium
Software Engineer Locked

Implement cd and merge intervals

This question evaluates proficiency in filesystem path normalization (string parsing, tokenization, and edge-case handling) and interval-merging algor...

Coding & Algorithms
3
0
57 people solved
Jan 16, 2026
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Meta
Medium
Machine Learning Engineer Locked

Implement exponentiation and fill grid distances

This question evaluates algorithmic problem-solving skills in the Coding & Algorithms domain, specifically numeric algorithms for fast exponentiation ...

Coding & Algorithms
4
0
86 people solved
Jan 8, 2026
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Meta
Hard
Software Engineer Locked

Count three-digit numbers with distinct digits

This question evaluates a candidate's understanding of digit manipulation, basic combinatorics, and implementation of value-range constraints in codin...

Coding & Algorithms
2
0
42 people solved
Jan 6, 2026
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Meta
Medium
Machine Learning Engineer Locked

Implement solutions to several coding tasks

This multipart prompt evaluates algorithmic problem-solving and data-structure design skills across several areas: finding a minimum in a rotated sort...

Coding & Algorithms
3
0
62 people solved
Jan 5, 2026
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Meta
Medium
Software Engineer Locked

Design a food ordering and delivery system

This question evaluates a candidate's ability to design scalable, reliable backend systems including API design, data modeling, state management, disp...

System Design
8
0
73 people solved
Jan 5, 2026
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Meta
Medium
Software Engineer Locked

Solve counting and frequency coding tasks

This question evaluates algorithmic skills in counting, frequency analysis, constraint-based pair counting, and stream-oriented tokenization and ranki...

Coding & Algorithms
3
0
43 people solved
Jan 5, 2026
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Meta
Medium
Data Scientist

Write SQL for CTR and revenue

Write SQL for the following two tasks. Problem 1: CTR during peak vs. non-peak hours You are given three tables: - ads(ad_id BIGINT, advertiser_id BIG...

Data Manipulation (SQL/Python)
6
1
42 people solved
Jan 3, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Implement power, reduce string, parse tree, debug maze

This multi-part question evaluates numerical algorithms (fast exponentiation), string-processing and reduction logic, recursive parsing and tree const...

Coding & Algorithms
3
0
49 people solved
Jan 1, 2026
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Meta
Medium
Software Engineer Locked

Maximize Letters from Disjoint Words

This question evaluates understanding of combinatorial selection, set-based constraints, and efficient representation and enumeration of candidate sub...

Coding & Algorithms
2
0
23 people solved
Dec 30, 2025
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Meta
Medium
Software Engineer

Answer four string/array/battery coding questions

You are given four independent coding questions. Problem A — Uppercase vs lowercase count difference Given a string s (ASCII), count how many characte...

Coding & Algorithms
4
0
46 people solved
Dec 16, 2025
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Meta
Easy
Software Engineer

Merge overlapping time intervals

Problem You are given a list of closed intervals intervals, where each interval is [start, end] and start <= end. Merge all intervals that overlap and...

Coding & Algorithms
2
0
34 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer

Validate near-palindrome and course prerequisite feasibility

Coding round (40 minutes): two problems You do not need to run code, but you should: - Explain your approach clearly. - Walk through the logic on exam...

Coding & Algorithms
2
0
30 people solved
Dec 15, 2025
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Meta
Medium
Machine Learning Engineer

Implement weighted random city and sparse dot product

Question 1: Weighted random city picker You are given a mapping from city → population (all populations are positive integers). Implement a random gen...

Coding & Algorithms
11
0
80 people solved
Dec 15, 2025
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Meta
Medium
Machine Learning Engineer

Solve shipping capacity and expression insertion

Problem A: Minimum shipping capacity You are given an array weights where weights[i] is the weight of the i-th package. Packages must be shipped in or...

Coding & Algorithms
10
0
92 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer

Convert 32-bit integer to hexadecimal

Problem Given a 32-bit signed integer num, return its hexadecimal representation as a string. Requirements - Use lowercase letters a-f. - Do not inclu...

Coding & Algorithms
1
0
25 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer

Return all root-to-leaf path sums

Problem You are given the root of a binary tree where each node contains an integer value (may be negative). Return an array/list containing the sum o...

Coding & Algorithms
2
0
40 people solved
Dec 15, 2025
Meta logo
Meta
Medium
Software Engineer

Explain ACID and isolation levels

Explain what a database transaction is, define the ACID properties (Atomicity, Consistency, Isolation, Durability), and describe common transaction is...

Software Engineering Fundamentals
7
0
69 people solved
Dec 8, 2025
Meta logo
Meta
Medium
Software Engineer

Validate complete binary tree

You are given the root of a binary tree. A complete binary tree is defined as a binary tree in which: 1. Every level, except possibly the last, is com...

Coding & Algorithms
1
0
23 people solved
Dec 8, 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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