Meta Interview Questions

Meta Behavioral & Leadership 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 Company08.08.2026
Showing 20 results
Role
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Self-Attention: Implementation, Complexity, and Efficient Variants

This question evaluates a machine learning candidate's understanding of the self-attention mechanism, including its implementation, computational comp...

Machine Learning
43
0
369 people solved
Jun 27, 2026
Meta logo
Meta
Medium
Software Engineer

Design a Concurrent, Memory-Bounded Tally Service

Design a Concurrent, Memory-Bounded Tally Service Design and implement the core of a TallyService with two operations: - bump(timestamp): record one e...

Software Engineering Fundamentals
8
0
85 people solved
Jul 1, 2026
Meta logo
Meta
Medium
Software Engineer

Make a String Palindromic with at Most K Deletions

Implement can_make_palindrome(text, k). Return whether deleting at most k characters from text can leave a palindrome. Characters that remain must pre...

Coding & Algorithms
0
0
8 people solved
Aug 8, 2026
Meta logo
Meta
Medium
Data Scientist

Evaluate a Live-Stream Group Notification Under Network Effects

Prompt A social travel app wants to add a notification: “Someone in one of your groups is live now.” The notification can increase attendance at live ...

Analytics & Experimentation
14
0
117 people solved
Jul 6, 2026
Meta logo
Meta
Medium
Software Engineer

Find a Local Minimum with Left-Biased Binary Search

Implement left_biased_local_min(values) for a nonempty integer array. An index is a local minimum when its value is no greater than each neighbor that...

Coding & Algorithms
1
0
7 people solved
Aug 8, 2026
Meta logo
Meta
Medium
Software Engineer

Design Boolean Search over User Status Posts

Design a search service for short plain-text status posts. It must ingest new statuses and answer Boolean queries containing terms joined by AND and O...

System Design
0
0
6 people solved
Aug 8, 2026
Meta logo
Meta
Medium
Software Engineer

Sum Numbers Formed by Root-to-Leaf Paths

Implement sum_root_to_leaf_numbers(values) for a binary tree serialized as a zero-based heap array. For an existing node at index i, its children are ...

Coding & Algorithms
0
0
6 people solved
Aug 8, 2026
Meta logo
Meta
Medium
Software Engineer

Count Subarrays with a Target Sum

Implement count_target_subarrays(values, target). Return the number of contiguous subarrays whose elements sum exactly to target. Values may be negati...

Coding & Algorithms
0
0
6 people solved
Aug 8, 2026
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Machine Learning Fundamentals: Optimizers, Scaling Laws, and Clustering

This question evaluates conceptual grasp of core machine learning fundamentals: gradient-based optimizers, neural scaling laws, and unsupervised clust...

Machine Learning
12
0
157 people solved
Jun 27, 2026
Meta logo
Meta
Medium
Software Engineer

Answer Exact Island-Size Existence Queries

Implement island_size_queries(grid, queries) for a rectangular binary grid. Cells containing 1 are land, cells containing 0 are water, and land cells ...

Coding & Algorithms
0
0
4 people solved
Aug 8, 2026
Meta logo
Meta
Medium
Software Engineer

Return a Shortest Path Through a Binary Grid

Implement shortest_grid_path(grid) for a square binary matrix. A cell containing 0 is open and a cell containing 1 is blocked. Start at the top-left c...

Coding & Algorithms
0
0
4 people solved
Aug 8, 2026
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Design an LLM-Based Coding Assistant

This question evaluates a candidate's ability to design an end-to-end machine learning system, covering model architecture, training data pipelines, e...

ML System Design
17
0
138 people solved
Jun 27, 2026
Meta logo
Meta
Medium
Data Scientist

Define Success for a New Group Feature Without Hiding Cannibalization

Prompt A travel-oriented social app is considering a new Groups feature that lets people who do not already know one another form communities around d...

Analytics & Experimentation
12
0
98 people solved
Jul 6, 2026
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Design an LLM-Based Conversational Assistant (Chatbot)

This question evaluates the ability to design an end-to-end LLM-based conversational assistant, covering pretraining, alignment, retrieval, and servin...

ML System Design
6
0
63 people solved
Jun 27, 2026
Meta logo
Meta
Medium
Software EngineerSenior+ Locked

Describe Using AI at Work

This question evaluates AI literacy, communication of technical impact, ethical judgment about safeguards, and the ability to quantify business or eng...

Behavioral & Leadership
6
0
83 people solved
May 16, 2026
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Find A Low-Quality Annotator From Label Data

Practice a pandas-style data analysis prompt for identifying a low-quality annotator from label data. The question emphasizes cleaning, agreement or g...

Data Manipulation (SQL/Python)
5
0
60 people solved
Jun 2, 2026
Meta logo
Meta
Hard
Machine Learning Engineer Locked

Implement 1NN Embeddings and Forward Pass

This question evaluates proficiency in vectorized linear algebra and neural-network forward-pass implementation within the Machine Learning domain, co...

Machine Learning
12
0
141 people solved
May 19, 2026
Meta logo
Meta
Medium
Software Engineer Locked

Design an Instagram-Style Social Feed

A Meta software-engineer onsite system-design question: design an Instagram-style social feed (photo/video sharing with a personalized home feed). It ...

System Design
7
0
87 people solved
May 14, 2026
Meta logo
Meta
Medium
Data Scientist

Compare Survey Satisfaction for New and Established Users

The interview report preserved the survey tables and the request to compare response levels for new and old users, but it explicitly noted that the in...

Data Manipulation (SQL/Python)
4
0
70 people solved
Jul 6, 2026
Meta logo
Meta
Medium
Data Scientist

Evaluate a New Ads-Ranking Algorithm

Evaluate a New Ads-Ranking Algorithm An ads team has developed a new ranking algorithm that chooses which ad to show for each eligible opportunity. En...

Analytics & Experimentation
1
0
36 people solved
May 22, 2026

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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