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
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Meta
Medium
Data Scientist

Describe a high-impact product project

In a conversation with a Head of Product, you are asked to discuss one project in depth. Describe a product or marketplace project where you had meani...

Behavioral & Leadership
6
0
74 people solved
Mar 11, 2026
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Meta
Medium
Data Scientist

Describe leadership and collaboration examples

For a Meta Data Scientist, Product Analytics interview, answer the following behavioral questions using concrete examples. For each one, explain the b...

Behavioral & Leadership
3
0
37 people solved
Mar 10, 2026
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Meta
Medium
Machine Learning Engineer Locked

Design a system to detect weapon posts

This question evaluates system design and machine learning engineering competencies, including multi-modal content detection, real-time model serving,...

System Design
8
0
65 people solved
Feb 11, 2026
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Meta
Medium
Software EngineerSenior+

Design Real-Time Auctions for Social Posts

Design a real-time auction feature for a large social photo and video app. A creator can attach an auction to a post. Viewers can place bids, and ever...

System Design
2
0
26 people solved
Feb 11, 2026
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Meta
Hard
Data Engineer

Write SQL for car rental utilization by city

SQL / Data Query Prompt (Car Rental) You are given four tables: user - user_id location - location_id - city car - car_id - car_size (e.g., compact, m...

Coding & Algorithms
12
2
178 people solved
Dec 1, 2025
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Meta
Medium
Product Analyst

How would you drive product growth?

Assume you are interviewing for a Product Growth Analyst role at Meta. Answer the following product growth and analytics cases. For each case, clarify...

Analytics & Experimentation
5
0
74 people solved
Jan 16, 2026
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Meta
Medium
Machine Learning Engineer

Describe handling intense time pressure

Behavioral & Leadership (Onsite): Thriving Under Time Pressure and Multitasking Prompt Tell me about a time you had to deliver high‑quality work under...

Behavioral & Leadership
11
0
83 people solved
Sep 6, 2025
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Meta
Medium
Machine Learning EngineerIntern AI Locked

Implement Sparse Matrix Operations

This question evaluates proficiency in sparse linear algebra, efficient algorithms, and data-structure design for numerical and machine learning workl...

Coding & Algorithms
6
0
48 people solved
Feb 8, 2026
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Meta
Hard
Data Scientist

Deploy multi-armed bandits safely

Online bandit with 3 variants, churn guardrail, and delayed conversions Context You are running an online experiment with 3 variants (including contro...

Machine Learning
8
0
70 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist Locked

Design a clustered A/B test with spillovers

This question evaluates a data scientist's understanding of cluster-randomized experiments with spillovers, covering causal inference under interferen...

Analytics & Experimentation
4
0
50 people solved
Oct 13, 2025
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Meta
Hard
Data Scientist

Compute sample size and test duration correctly

Powering Two Online Experiments: Sample Size, Duration, and Design Defenses You are designing experiments to improve a friend-accept rate metric in a ...

Statistics & Math
3
0
59 people solved
Oct 13, 2025
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Meta
Medium
Software Engineer

Merge Two Lists of Non-Overlapping Intervals

You are given two lists of closed integer intervals, a and b. Each interval is represented as a pair [start, end] with start <= end, and it covers eve...

Coding & Algorithms
1
0
13 people solved
Mar 6, 2026
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Meta
Medium
Software Engineer

Solve peak element and unique word abbreviation

You are given two independent coding problems to solve. 1) Peak element in an array (binary search) - Input: an integer array nums of length n >= 1. -...

Coding & Algorithms
2
0
38 people solved
Feb 6, 2026
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Meta
Hard
Product Manager

Meta PM Interview Questions

Product and Decision-Making Onsite Case Prompt Set You are a Product Manager candidate preparing for a mixed onsite loop covering strategy, data, desi...

Product / Decision Making
107
0
896 people solved
Jul 4, 2025
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Meta
Easy
Data Scientist

Compute probabilities for chatbot response quality

Context A chatbot response is considered good if it is both: - Helpful, and - Honest. You are told: - \(P(\text{Helpful}) = 0.8\) - \(P(\text{Honest})...

Statistics & Math
4
1
89 people solved
Oct 30, 2025
Meta logo
Meta
Medium
Machine Learning Engineer

Answer impact, conflict, and difficult coworker questions

Behavioral questions 1. Describe the most impactful project you have worked on. 2. Tell me about a difficult person you have worked with. 3. Describe ...

Behavioral & Leadership
4
0
62 people solved
Dec 15, 2025
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Meta
Medium
Software Engineer

Describe failures, self-reflection, and conflict resolution

Answer the following behavioral prompts with recent examples: 1. Self-reflection / improvement - “In a recent project, where could you have done be...

Behavioral & Leadership
16
0
122 people solved
Jan 6, 2026
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Meta
Medium
Data Engineer Locked

Define metrics and data model for product features

This question evaluates a candidate's product sense and data engineering competency, including defining core success metrics, designing dashboard visu...

System Design
16
0
131 people solved
Mar 1, 2026
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Meta
Medium
Data Scientist

Compute seller counts and vehicle share

You are given two tables: 1. listing_interactions - buyer_id BIGINT - seller_id BIGINT - event_date DATE - product_id BIGINT - listing_...

Data Manipulation (SQL/Python)
6
0
51 people solved
Jan 5, 2026
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

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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