Meta System Design 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.
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Write dating profile report with final reviews
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Analyze ad targeting expectations and distributions
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Choose KPIs for short-video recommendations
This question evaluates a data scientist's ability to define precise product metrics, set guardrails, design and power A/B tests, and apply weighted d...
Decide event notification launch via experiments
This question evaluates a data scientist's competency in experimentation design, causal inference under network interference, metric engineering, and ...
Choose ML metrics under asymmetric costs
This question evaluates a data scientist's competency in cost-sensitive binary classification, covering skills such as defining business cost matrices...
Model preference without ground truth
This question evaluates a data scientist's competency in uplift modeling, causal inference, experimental design, weak supervision, and bias and shift ...
Brainstorm how to optimize email engagement
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Design experiment with network and novelty effects
This question evaluates a data scientist's competence in experimental design and causal inference under network interference and novelty effects, cove...