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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.
- 9403
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- 546
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- 11
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- 8
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Showing 4981–5010 of 9403 results
- 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 - MetaMediumSoftware EngineerDesign a mini banking system
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a mini banking system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - 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 - MetaMediumSoftware EngineerDesign a ranking and recommendation system
This problem tests your ability to design a multi-stage recommendation pipeline that balances low-latency retrieval with high-precision ranking at scale.
System DesignTechnical ScreenMachine LearningRecommendation SystemsScalability - MetaMediumSoftware EngineerDesign a real-time ad impression aggregator
This question evaluates a candidate's ability in scalable real-time system design, covering competencies such as stream processing, event aggregation, time-windowed metrics, handling late and out-of-order events, and designing for very high write throughput.
System DesignTechnical Screen - 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 - MetaHardSoftware EngineerDesign a scalable banking system
This problem tests your ability to design a distributed, high-concurrency ledger system that maintains strict consistency and atomicity for financial transactions.
System DesignTechnical ScreenDistributed SystemsACIDDatabase Design - 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-ad harmful content detection system
This question assesses the ability to architect a scalable, multi-modal machine learning pipeline for detecting policy-violating content (weapon advertisements) within a high-throughput ad-delivery system.
System DesignTechnical ScreenMulti-modal MLComputer VisionNLP - 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 - MetaHardData ScientistDesign an A/B Test for Group Video Calls Impact
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 A/B Test for Group Video Calls Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - MetaHardData ScientistDesign an A/B test for pinned-unread feature
This question evaluates proficiency in experiment design and causal inference for product analytics, including choosing the randomization unit, defining eligibility and exposure, selecting primary and guardrail metrics, performing power/MDE and sample-size analysis, and addressing identity cross-contamination and network effects.
Analytics & ExperimentationTechnical Screen - MetaHardSoftware EngineerDesign an Automated Ticket Investigation Agent
This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.
ML System DesignTechnical Screen - 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 Online Game Leaderboard
A Meta software-engineer system-design screen: design the backend for an online game leaderboard supporting score recording, a global top-N board, a friends board, a player's rank and nearby players, and daily/weekly/seasonal/all-time windows at high write volume and low read latency. The model answer separates a durable score event log from an in-memory sorted ranking store, computes the friends board on read, and covers tie-breaking, caching, sharding, consistency trade-offs, anti-cheat, and rebuilds.
System DesignTechnical Screen
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