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
Meta logo
Meta
Medium
Data Scientist Locked

Measure and mitigate notification spam

This question evaluates a data scientist's competency in defining precise success and guardrail metrics, designing counterfactual-aware experiments an...

Analytics & Experimentation
3
0
34 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Evaluate Facebook Dating launch and validate success

Validation Plan: Scaling Facebook Dating from Pilot to Broader Rollout Context: You are a data scientist evaluating whether a limited-market pilot of ...

Analytics & Experimentation
5
1
41 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Clarify scope and align to mission

Clarify and Align: New Google Maps Feature to Boost Group Page Engagement Context (Completed) Assume "Group pages" are shared spaces in Google Maps wh...

Behavioral & Leadership
6
0
48 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Select and prioritize metrics with guardrails

Design a Metrics Framework for a New Groups Stories Feature Context You are evaluating a new Groups Stories feature whose goal is to increase meaningf...

Analytics & Experimentation
1
0
30 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Diagnose sudden KPI drop with segmentation

Production Incident: 10% Drop in Daily Likes (DAU Flat) on 2025-09-01 You are investigating a 10% day-over-day drop in daily Like actions on a global ...

Analytics & Experimentation
5
0
43 people solved
Oct 13, 2025
Meta logo
Meta
Easy
Data Scientist

Quantify base-rate dilution in CTR

Weighted-Average CTR and Volume Requirements You are assessing the impact of introducing a new high-CTR event notification into an existing stream of ...

Statistics & Math
3
0
32 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Brainstorm how to optimize email engagement

Lifecycle Email: Increase Incremental On‑Site Engagement You own lifecycle email for a large consumer app and are tasked with increasing on‑site engag...

Analytics & Experimentation
3
0
51 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Deliver an elevator pitch and impact example

Elevator Pitch + End-to-End Experimentation Case + “Why Meta?” Context You are interviewing for a Data Scientist role during a technical screen. Use c...

Behavioral & Leadership
4
0
34 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Diagnose a sudden KPI drop and validate causes

A core KPI (comments_per_DAU) suddenly drops materially. Outline a structured root-cause analysis and validation plan. a) Scoping and sanity: Quantify...

Analytics & Experimentation
2
0
30 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Design and validate ad model launch

You are on the Ads team and just trained a new ad recommendation model meant to replace the current model in production. Design a rigorous plan to dec...

Analytics & Experimentation
2
0
34 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Quantify launch decision with tests and guardrails

You will formalize the statistical decision rules for the Instagram button experiment described above. Given: baseline exploration rate (p0) = 0.15 pe...

Statistics & Math
3
1
48 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Design pre-launch plan and cluster A/B test

A Facebook feature ('More like this' button that surfaces similar products) is being considered for Instagram, but it has not launched on Instagram. Y...

Analytics & Experimentation
3
0
45 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Estimate revenue of organic shopping tab

Estimate Monthly Revenue for a New Shopping Tab (Organic Only) Context You are evaluating the potential monthly revenue impact of launching a new Shop...

Analytics & Experimentation
4
0
34 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Produce dating profile funnel report by cohort

You work on a dating app. Produce a daily profile-funnel report for 2025-08-25 through 2025-09-01 inclusive, with one row per day, gender, and age_buc...

Data Manipulation (SQL/Python)
1
0
10 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design an A/B test for WhatsApp call reliability

A/B Test Design: Adaptive Codec for Unstable Networks (WhatsApp Calling) Context You join the Calling organization. A PM proposes enabling an adaptive...

Analytics & Experimentation
5
0
54 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Choose metrics for fake-user classifier

Classifying Fake Accounts: Metrics, Capacity, Thresholding, and Validation Context - Population: 10,000,000 daily active users (DAU) - True fake rate ...

Machine Learning
2
0
45 people solved
Oct 13, 2025
Meta logo
Meta
Medium
Data Scientist

Analyze skewed comments and sampling effects

Right‑Skewed Daily Comments: Location Stats and Sampling Distributions You’re analyzing daily user comments per user, which are right‑skewed count dat...

Statistics & Math
5
0
45 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Demonstrate leadership under ambiguity

Behavioral & Leadership Prompt (Data Scientist) Describe one high-stakes project where priorities changed mid-stream and you had to influence without ...

Behavioral & Leadership
4
0
43 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Design a small-sample launch experiment in Europe

Launch Test Design: Early-Access EU Businesses Context You have an early-access pool of 1,200 EU businesses for a new chat subscription offering. Chat...

Analytics & Experimentation
3
0
28 people solved
Oct 13, 2025
Meta logo
Meta
Hard
Data Scientist

Define success metrics and guardrails for B2B chat

Define a Success-Measurement Plan for a New EU B2C Chat Subscription You are launching a paid business-to-customer chat subscription in the EU. Design...

Analytics & Experimentation
7
0
67 people solved
Oct 13, 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.

Explore more Meta interview questions

Jump straight to Meta questions for a specific role or category.

By role
By category
In-depth guides
Across all companies

Featured Meta interview prep guides

Concept walkthroughs, worked examples, and the real questions from candidate reports.

Editorial prep
Product Manager
Meta interview
Read the guide
Editorial prep
Software Engineer
Meta interview
Read the guide