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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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- 546
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- 11
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Showing 1981–2010 of 3640 results
- MetaMediumSoftware EngineerDesign a flight search platform
This question tests the ability to design a high-scale search platform that aggregates volatile data from heterogeneous third-party providers while maintaining low latency and high availability.
System DesignOnsiteDistributed SystemsAPI DesignCaching - MetaMediumSoftware EngineerDesign a game leaderboard service
This question tests the ability to design a high-throughput, low-latency leaderboard system, focusing on data structures for ranking, storage trade-offs, and scalability under heavy write and read loads.
System DesignOnsiteScalabilityData StructuresCaching - MetaMediumSoftware EngineerDesign a large-scale web crawler
This question evaluates a candidate's ability to design scalable, fault-tolerant distributed systems for web-scale data acquisition, covering competencies such as crawler architecture, URL frontier management, deduplication, per-domain rate limiting and robots.
System DesignOnsite - MetaMediumSoftware EngineerDesign a live video comments system
This problem tests your ability to design a high-throughput, real-time messaging system capable of handling sudden spikes in concurrent viewers and maintaining low latency for chat delivery.
System DesignOnsiteWebSocketsPub/SubScalability - MetaMediumSoftware EngineerDesign a marketplace price tracking service
This question evaluates a candidate's ability to design scalable, reliable ingestion and processing pipelines, time-series price storage and querying, deduplication across users, and notification/alert mechanisms for a large-scale price-tracking service.
System DesignOnsite - MetaMediumMachine Learning EngineerDesign a newsfeed dislike model
This problem tests the ability to design an end-to-end ML system for predicting user dissatisfaction, focusing on label definition, feature engineering, and the integration of negative signals into a ranking pipeline.
ML System DesignOnsiteRecommender SystemsRankingImbalanced Learning - MetaMediumSoftware EngineerDesign a post privacy/visibility system
This question evaluates a candidate's ability to design access control and visibility mechanisms for social posts, covering privacy models, efficient read-time enforcement, data modeling, caching strategies, and handling dynamic friendship and privacy updates.
System DesignOnsite - MetaMediumSoftware EngineerDesign a price drop tracking service
This question evaluates system-design competencies such as scalable architecture, data modeling for time-series price history, external data ingestion and integration, alerting and monitoring, and trade-off reasoning for large-scale services.
System DesignOnsite - MetaHardSoftware EngineerDesign a product notifications system
This question evaluates system design competencies for building a large-scale notifications platform—covering architecture, APIs, data modeling, event pipelines, delivery semantics (retries, deduplication, idempotency), targeting/segmentation, localization, monitoring, and legal compliance such as GDPR/CCPA.
System DesignOnsite - MetaHardSoftware EngineerDesign a real-time messenger
A Meta software-engineer system-design onsite question: design a real-time messenger supporting 1:1 and group chats at 100M MAU with p99 send-to-receive latency under 150 ms. It covers APIs, message ordering and idempotency, delivery semantics, multi-device sync, WebSocket fanout, storage/search, E2EE, abuse controls, retention/GDPR, multi-region failover, and observability.
System DesignOnsite - MetaHardMachine Learning EngineerDesign a recommendation system
This question evaluates competence in designing large-scale recommendation systems and ML engineering skills such as candidate generation, feature store design (offline/online and point-in-time correctness), real-time signal ingestion, ranking and re-ranking, online exploration, cold-start strategies, feedback-bias mitigation, experimentation, and operational reliability. It is commonly asked to assess the ability to make architecture-level trade-offs for scalability, latency and throughput targets, multi-objective optimization, data governance and outage fallback behavior; it falls under the System Design and Machine Learning domain and requires practical, architecture-level application with conceptual trade-off reasoning rather than low-level implementation detail.
System DesignOnsite - MetaHardMachine Learning EngineerDesign a scalable MoE pretraining pipeline
This question evaluates expertise in large-scale ML system design and distributed training for Mixture-of-Experts (MoE) models, covering competencies in model architecture, parallelism and communication strategies, memory and optimizer sharding, data curation and tokenization, monitoring, checkpointing, and fault tolerance; it is categorized under ML System Design and targets practical application with systems-level architectural reasoning rather than purely theoretical concepts. It is commonly asked to evaluate the ability to balance throughput, stability, and cost when scaling pretraining to hundreds of GPUs, reason about routing and capacity trade-offs, and design production-grade pipelines that can extend to larger clusters.
ML System DesignOnsite - MetaHardData ScientistDesign a small-sample launch experiment in Europe
This question evaluates a data scientist's competencies in experimental design, causal inference, estimand specification, power and MDE calculations for heavy-tailed and overdispersed outcomes, interference handling, compliance/IV considerations, and sequential monitoring in field experiments.
Analytics & ExperimentationOnsite - MetaHardSoftware EngineerDesign a ticketing purchase system
This question evaluates understanding of large-scale system design and distributed systems concepts, including concurrency control, consistency models, real-time fanout, payment idempotency, and failure handling for high-concurrency ticketing with interactive seat maps.
System DesignOnsite - MetaMediumMachine Learning EngineerDesign a weapon-sale ad detection system
This question tests the ability to architect an end-to-end multimodal classification pipeline for detecting policy-violating content at scale while balancing precision, recall, and operational latency.
ML System DesignOnsiteMultimodal LearningContent ModerationClassification - MetaMediumSoftware EngineerDesign ad click tracking and dashboard at scale
This problem tests your ability to design a high-throughput, fault-tolerant ingestion pipeline and an aggregation service for real-time analytics dashboarding.
System DesignOnsiteDistributed SystemsData PipelinesScalability - MetaMediumSoftware EngineerDesign an Expected O(1) Randomized Container
Design a distinct-element container with expected constant-time insertion and uniformly random removal. The discussion covers mutation invariants, uniformity, empty-state behavior, expected complexity, deterministic randomness tests, and how duplicate or concurrent variants would change the contract.
Software Engineering FundamentalsOnsite - MetaHardData ScientistDesign an Experiment to Evaluate New ML Model
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design an Experiment to Evaluate New ML Model states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign an Experiment to Evaluate New Recommendation Model
This problem tests the ability to design an end-to-end A/B test framework for a machine learning model update, focusing on metric selection, causal validity, and post-experiment analysis.
Analytics & ExperimentationOnsiteA/B TestingExperimentationMetric Design - MetaHardSoftware EngineerDesign an Instagram-like photo-sharing platform
This question evaluates system design competencies including distributed systems, scalability and capacity planning, data modeling and storage strategies, API design, media processing and CDN integration, consistency models, operational reliability, observability, and estimation.
System DesignOnsite - MetaHardMachine Learning EngineerDesign an LLM-Based Coding Assistant
Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.
ML System DesignOnsiteLLMInference ServingCode Generation - MetaHardMachine Learning EngineerDesign an LLM-Based Conversational Assistant (Chatbot)
Design an end-to-end conversational AI assistant powered by large language models, covering post-training alignment, retrieval/tool orchestration, low-latency serving, and safety guardrails at consumer scale.
ML System DesignOnsiteLLMRAGModel Serving - MetaMediumSoftware EngineerDesign an On-Demand Delivery Platform
This question evaluates skills in designing scalable, reliable distributed systems with emphasis on state management (shopping cart persistence), real-time telemetry (courier location updates), workflow orchestration (order lifecycle and dispatch), and operational concerns like scalability and reliability.
System DesignOnsite - 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 - 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 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.
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