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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
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Showing 5011–5040 of 9403 results
- MetaHardSoftware EngineerDesign an ad click tracking system
This problem tests your ability to design a high-scale, fault-tolerant distributed system capable of processing massive volumes of real-time ad click events while ensuring data consistency and accuracy.
System DesignOnsiteDistributed SystemsHigh-throughputData Engineering - MetaMediumData ScientistDesign an ad recommendation and ranking system
This question evaluates end-to-end ad recommendation and ranking design skills, covering machine learning modeling, recommender systems, causal and counterfactual evaluation, online learning, and production ML engineering within the Machine Learning domain, at a high-level system-design and modeling abstraction.
Machine LearningOnsite - MetaHardData ScientistDesign an experiment to evaluate a new ads algorithm
This question evaluates a data scientist's competency in experimental design, causal inference, metric selection and trade-offs, randomization and interference reasoning, rollout strategies, and observational methods for algorithm evaluation, including consideration of stakeholder impacts such as users, advertisers, and revenue.
Analytics & ExperimentationTechnical Screen - MetaMediumMachine Learning EngineerDesign an image copyright-violation detection system
This problem evaluates your ability to design a scalable, end-to-end computer vision pipeline capable of identifying copyrighted content within user-uploaded images, including scenarios involving cropping, filters, and collages.
ML System DesignOnsiteComputer VisionEmbeddingsSimilarity Search - MetaMediumSoftware EngineerDesign an in-memory cloud file system
This question evaluates understanding of object-oriented design, stateful data structures, and resource management including user quotas, ownership semantics, and file metadata operations.
Coding & AlgorithmsOnline Assessment - MetaHardSoftware EngineerDesign an in-memory cloud storage system
This question tests your ability to design a high-performance, in-memory distributed metadata store that supports hierarchical file structures, quota management, and efficient retrieval of top-k largest files.
System DesignOnline AssessmentData StructuresConcurrencyTrie - MetaMediumSoftware EngineerDesign an in-memory database with TTL and history
This question evaluates understanding of time-versioned in-memory key–field–value stores, covering TTL-based expiration, historical reads, atomic compare-and-set/delete semantics, efficient scan/scan-by-prefix operations, and time/space complexity analysis, and it belongs to the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - MetaMediumSoftware EngineerDesign an online auction system
A Meta software engineer onsite system design question: build an eBay-style online auction platform supporting proxy bidding, reserve prices, buy-it-now and soft close at 500k concurrent auctions and 5M watchers with p99 bid-to-UI latency under 200 ms. The answer covers APIs, data model, the proxy-bid pricing algorithm, single-writer-per-auction consistency, idempotent bid placement, real-time push trade-offs (polling vs long polling vs SSE vs WebSockets), sharding, finalization and recovery. It also covers anti-sniping, fraud, escrow payments, auditability and capacity estimation.
System DesignOnsite - MetaMediumSoftware EngineerDesign an online chess game with undo and leaderboard
This problem tests the ability to design a low-latency, stateful system for a turn-based game, focusing on synchronization, undo history management, and scalable real-time leaderboard updates.
System DesignOnsiteReal-timeConcurrencyWebSockets - MetaHardSoftware EngineerDesign an online coding judge
This problem tests the ability to architect a distributed, multi-tenant system for sandboxed execution of untrusted code, emphasizing security, scalability, and performance isolation.
System DesignOnsiteSecurityDistributed SystemsSandboxing - MetaMediumSoftware EngineerDesign an online coding platform
This question evaluates a candidate's ability to design scalable, secure distributed systems with emphasis on architecture, data modeling, service design, sandboxed code execution, and performance under high load for an online coding platform.
System DesignOnsite - MetaHardData ScientistDesign analytics and experiment for group video calls
This question evaluates product analytics and experimentation competencies, including tie-strength and group-affinity measurement, event-level instrumentation design, proxy-product forecasting, participant-cap constraints and operational trade-offs, and experiment design that accounts for network effects and contamination.
Analytics & ExperimentationTechnical Screen - MetaHardSoftware EngineerDesign and Evaluate a Solver for a 36-Card Sum-Fifteen Game
Tests state-space search (DFS with memoization), heuristic design, and simulation benchmarking for a deterministic 36-card solitaire game.
Software Engineering FundamentalsOnsiteBacktrackingGame TheoryHeuristics - MetaMediumData ScientistDesign and analyze a group-calls experiment
This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign and evaluate P2P payments in messaging
This question evaluates a data scientist's competency in product analytics, causal inference, experimental design under network interference, monetization measurement (ARPU impact), and risk mitigation for a peer-to-peer payments feature relevant to a Data Scientist role.
Analytics & ExperimentationOnsite - MetaEasyAnalytics EngineerDesign and evaluate an ads ranking algorithm
This problem tests your understanding of the end-to-end machine learning lifecycle for an ads ranking system, focusing on feature engineering, model selection, and the transition from offline metrics to online business impact.
Machine LearningOnsiteAds RankingMetricsEvaluation - MetaMediumData ScientistDesign and validate ad model launch
This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign and validate an ads feed experiment
This question tests the ability to design a rigorous A/B test for a core product feature, covering metric selection, interference handling, and post-experiment analysis under complex constraints.
Analytics & ExperimentationOnsiteA/B TestingCausal InferenceProduct Metrics - MetaHardData EngineerDesign batch and streaming ETL architecture
Design batch and streaming ETL architecture evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - MetaHardData ScientistDesign cluster-randomized test under network effects
This question evaluates a data scientist's experiment design and causal inference skills under interference, covering exposure and estimand definition, graph-based cluster construction, cluster-level randomization and contamination mitigation, metric selection, and design-effect/sample-size calculations.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerDesign coding platform with global leaderboard
This question tests the ability to design a high-throughput, real-time leaderboard system that balances strong consistency for rankings with the scalability required for a global user base.
System DesignOnsiteScalabilityCachingDatabase Partitioning - MetaMediumMachine Learning EngineerDesign comment ranking for a news feed
This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.
ML System DesignOnsiteRankingRecommendation SystemsMulti-task Learning - MetaHardData ScientistDesign experiment for Group Calls with interference
This question evaluates a data scientist's competency in experimental design and causal inference for networked features, covering metrics selection (primary and guardrail metrics), strategies for mitigating interference via randomization or quasi-experimental approaches, exposure and contamination controls, rollout planning, and pre-registration and power analysis. Commonly asked in Analytics & Experimentation interviews for Data Scientist roles, it assesses reasoning about bias–variance trade-offs, engineering constraints, and validity diagnostics in interconnected user environments, requiring both conceptual understanding of identification and practical application of experiment implementation.
Analytics & ExperimentationTechnical Screen - MetaEasyProduct AnalystDesign experiments and diagnose metric changes
This question evaluates product analytics and experimentation skills, including experiment design, metric framework definition, causal reasoning, diagnostic analysis, and marketplace operations trade-offs within the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign experiments under network interference
This question evaluates a data scientist's competence in experimental design and causal inference under network interference, assessing understanding of clustering strategies, exposure and exclusion definitions, contamination control, design effect and sample-size implications, estimation of intra-cluster correlation, and mixed-effects analysis for evolving clusters. Commonly asked in Analytics & Experimentation interviews to probe the ability to preserve internal validity while managing operational feasibility in two-sided marketplaces, it tests both conceptual understanding of interference and practical application skills in design and analysis planning.
Analytics & ExperimentationOnsite - MetaHardMachine Learning EngineerDesign image and multimodal generation systems
This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.
ML System DesignTechnical Screen - MetaHardSoftware EngineerDesign input validation and error handling
A Meta software engineer system design technical screen: design the API and system-level input validation and error-handling strategy for a service that computes the top-k most frequent elements in a posted array. The merged answer covers the request/response contract with a worked example, payload and cardinality limits, layered validation across client, gateway and service, authentication, rate limiting, idempotency, timeouts and retries, the full 4xx/5xx error taxonomy with sample error responses, partial-failure modes, graceful degradation to approximate top-k with stated error bounds, defenses against compression bombs and poisoned payloads, observability, and a test and production monitoring plan.
System DesignTechnical Screen - MetaMediumData ScientistDesign marketplace experiments at DoorDash
This question evaluates a candidate's skills in experimental design, product and marketplace analytics, causal inference, and diagnostic data science for multi-sided platforms.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign metrics for violating content exposure
This question evaluates competency in metric design and measurement for content safety, including defining exposure metrics, inclusion/exclusion rules, handling label latency and appeals, uncertainty-aware alerting, and A/B test design for a Data Scientist role in the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen - MetaEasyData ScientistDesign metrics to detect harmful content and fraud
This problem tests the ability to design robust monitoring metrics for detecting fraudulent user activity and policy-violating content, focusing on trade-offs between precision and recall.
Analytics & ExperimentationOnsiteAnalyticsMetricsFraud-Detection
Meta