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
Data Scientist

Design "Restaurants You May Know" Recommendation Algorithm

Design "Restaurants You May Know" Recommendation Algorithm A food-delivery app wants to launch a personalized home-page module called "Restaurants You...

Analytics & Experimentation
36
0
127 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Revenue Shifts to Identify Cannibalization Effects

Analyze Revenue Shifts to Identify Cannibalization Effects Management observes strong revenue growth from one creation_source, such as a channel where...

Analytics & Experimentation
18
0
50 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Compute Daily Revenue by Creation Source

active_ads date | ad_id | advertiser_id | creation_source | revenue 2023-09-01 | 1001 | 17 | mobile_app | 150.00 2023-09-01 | 1...

Data Manipulation (SQL/Python)
67
0
230 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Video Call Usage Metrics by Country and Date

video_calls +---------+-----------+------------+---------+----------+ | caller | recipient | ds | call_id | duration | +---------+-----------...

Data Manipulation (SQL/Python)
83
0
150 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Unique Callers and French Customer Call Percentage

video_calls +---------+-----------+--------------+---------------------+---------------+ | call_id | caller_id | recipient_id | start_ts | ...

Data Manipulation (SQL/Python)
86
0
276 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist Locked

Identify Potential Users for Instagram Shopping Tab Adoption

Evaluates how to identify likely adopters of an Instagram Shopping tab and measure whether the feature creates incremental commerce value. Strong answ...

Analytics & Experimentation
33
0
91 people solved
Jul 12, 2025
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Meta
Medium
Data Scientist

Determine Facebook's Restaurant Recommendation Viability Using Data

Determine Facebook's Restaurant Recommendation Viability Using Data Facebook may launch a restaurant-recommendation product that helps people discover...

Analytics & Experimentation
6
0
29 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Identify Users Interested in Group Video Calls

video_calls caller | recipient | ds | call_id | duration u1 | u2 | 2023-09-01| c100 | 320 u3 | u4 | 2023-09-01| c101 ...

Data Manipulation (SQL/Python)
113
0
363 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Employ Collaborative Filtering for Personalized Recommendation Lists

Collaborative Filtering and Ranking for Personalized Recommendation Lists You are releasing a new recommendation feature that must generate personaliz...

Machine Learning
40
0
128 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist Locked

Evaluate Chatbot Launch: Value, Risks, Impact, Success Metrics

Meta analytics prompt on evaluating a retailer-facing chatbot launch, covering opportunity sizing without A/B testing, user and business metrics, mode...

Analytics & Experimentation
54
0
73 people solved
Jul 12, 2025
Meta logo
Meta
Hard
Data Scientist

Uncover User Needs for Group Calling Effectively

Uncover User Needs and Measure Group Calling Impact You are the product analyst for a messaging platform planning to introduce group calling. You need...

Analytics & Experimentation
113
0
303 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze Distribution of Daily Page Shares Per User

Engagement Distributions and Cohort Dynamics You are analyzing per-user, per-day engagement. Assume the panel includes all users, inactive days count ...

Statistics & Math
87
2
127 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Calculate Probabilities for Mixed Reviewer Types

Probabilities for Mixed Reviewer Types Two types of reviewers exist in a marketplace: - Lazy reviewers are 20% of reviewers and always give good revie...

Statistics & Math
84
0
220 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Determine Key Statistics for Article Comment Distribution Analysis

Analyze Comment Counts per Article You are analyzing the distribution of the number of comments each article receives on a content website. You have c...

Statistics & Math
86
1
183 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Resolve Team Conflicts to Improve Delivery Efficiency

Behavioral Interview: Resolve Team Conflict and Improve Delivery You are in a Behavioral and Leadership interview for a Data Scientist role. The inter...

Behavioral & Leadership
32
0
104 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Convince Product Manager to Launch 'Show Similar Products' Button

Convince a PM to Test a "Show Similar Products" Button Instagram is considering adding a "Show similar products" button on product-tagged content to b...

Analytics & Experimentation
5
0
44 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Data Scientist

Leverage Data Sources for Effective Push Notification Strategy

Data Sources and Metrics for Push Notification Strategy A product team wants to improve the quality and impact of mobile push notifications for a cons...

Analytics & Experimentation
8
0
36 people solved
Jul 12, 2025
Meta logo
Meta
Medium
Product Manager

Behavioral Stories: Technical Depth, Data Experimentation, Failure

Behavioral Stories: Technical Depth, Experimentation, and Learning From Failure Prepare three onsite behavioral stories for a Product Manager role on ...

Behavioral & Leadership
9
0
51 people solved
Jul 4, 2025
Meta logo
Meta
Hard
Product Manager

Meta Product Design Trio

Product Strategy and MVP Prompt: Three Product Design Scenarios You are interviewing for a PM role. For each scenario below, craft a product strategy ...

Product / Decision Making
10
0
38 people solved
Jul 4, 2025
Meta logo
Meta
Hard
Product Manager

Define Meta Pay Success

Meta Pay is a payments product used across Meta's ecosystem. Define how you would determine whether Meta Pay is successful, then make a product priori...

Product / Decision Making
3
0
29 people solved
Feb 22, 2024

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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