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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
- Questions
- 546
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- 11
- Topics
- 8
- Languages
Showing 1951–1980 of 3640 results
- MetaMediumSoftware EngineerDescribe resolving conflict by persuading others
This question evaluates persuasion, conflict-resolution, communication, and stakeholder-management competencies and falls within the Behavioral & Leadership domain.
Behavioral & LeadershipOnsite - MetaEasyData ScientistDescribe something you did to be inclusive
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe something you did to be inclusive states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerDesign 24-Hour Ephemeral Stories for a Photo and Video Sharing App
A system design question on ephemeral stories that followers can view for 24 hours before they disappear, covering media upload, the tray of accounts with unwatched stories, playback, and viewer tracking. It tests expiry semantics, fan-out for popular authors, watched-state storage, and media delivery.
System DesignOnsite - MetaHardData ScientistDesign A/B test and success metrics for new feature
This question evaluates a data scientist's competency in experimental design, metric selection and guardrails, power and sample-size calculations, logging and attribution specifications, and revenue impact estimation for a new feature on a social media platform.
Analytics & ExperimentationOnsite - MetaMediumData ScientistDesign B2C chatbot success metrics and test plan
This question evaluates a data scientist's ability to design product success metrics and a rigorous experiment plan, covering KPI formulation, guardrail metrics and thresholds, randomization and experiment design, logging instrumentation, and quasi-experimental causal inference.
Analytics & ExperimentationOnsite - MetaHardSoftware EngineerDesign Boolean Search for Status Posts
This problem tests the ability to design an inverted index system that supports complex logical queries, high-concurrency updates, and consistent pagination over a distributed dataset.
System DesignOnsiteInverted IndexSearchDistributed Systems - MetaMediumSoftware EngineerDesign Boolean Search over User Status Posts
This problem tests the design of a scalable inverted index system capable of handling boolean queries (AND/OR/NOT) over a high-throughput stream of user status updates.
System DesignOnsiteInverted IndexBoolean SearchConcurrency - MetaHardData ScientistDesign Experiment to Measure Shopping Feature Impact
Meta data scientist experimentation prompt on measuring Instagram Shopping impact with A/B tests, geo or cluster holdouts, engagement and revenue metrics, guardrails, selection bias, network effects, and rollout decisions.
Analytics & ExperimentationOnsite - MetaHardData ScientistDesign Machine Learning Model for Facebook Groups Post Ranking
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign Real-Time Auctions for Social Posts
This question evaluates skills in designing scalable, low-latency real-time systems, including distributed architecture, concurrency control and ordering, data modeling, API design, and fault-tolerance for high-contention auction workloads.
System DesignOnsite - MetaHardSoftware EngineerDesign Ride Requests, Driver Matching, and Trip State
Design a high-scale real-time ride-hailing dispatch and trip lifecycle backend. The problem tests geospatial indexing, high-throughput location ingestion, concurrent driver assignment without double-booking, and idempotent distributed state management.
System DesignOnsiteDistributed SystemsGeospatialConcurrency - MetaMediumData ScientistDesign Sampling Strategy to Estimate Fake News Proportion
Design Sampling Strategy to Estimate Fake News Proportion evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnsite - MetaMediumSoftware EngineerDesign a Code Review Agent
This question evaluates a candidate's ability to design ML-powered developer tooling, covering competencies in LLM orchestration, deterministic tool integration, repository context management, code-analysis heuristics, safety and validation strategies, and inference cost estimation.
ML System DesignOnsite - MetaHardSoftware EngineerDesign a Dropbox-like file storage and sync service
Design a Dropbox-like file storage and sync service 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 - MetaMediumMachine Learning EngineerDesign a Large-Scale Personalized Recommendation System End to End
An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign a Messenger-Style Chat Backend Without WebSockets or Persistent Connections
Design a Messenger-style chat backend for one-to-one conversations without WebSockets or other persistent connections, using only request/response web APIs. Tests message ordering, idempotent sends, cursor-based sync, cheap polling, push wake-ups, multi-device delivery and failure handling at very large scale.
System DesignOnsite - MetaMediumSoftware EngineerDesign a Notification System That Delivers Events to Users at Scale
A system design question on a notification platform that takes events from many product services and delivers them to users through push and in-app channels. It tests ingestion contracts, preference checks, grouping and rate limiting, priority isolation, third-party push delivery, and deduplication under retries.
System DesignOnsite - MetaMediumSoftware EngineerDesign a People-You-May-Know Friend Recommendation System
A system design question on recommending people a user may know on a large social network, from candidate generation through ranking and serving. It tests graph-based candidate sourcing, batch versus real-time freshness, ranking signals and labels, filtering, privacy, cold start, and evaluation.
System DesignOnsite - MetaMediumSoftware EngineerDesign a Ranked Home News Feed for a Large Social Network
A system design question on building the home news feed of a large social network: publishing posts and serving each user a ranked, paginated feed of posts from the accounts they follow. It tests fan-out strategy, accounts with huge follower counts, ranking under a latency budget, and correctness for deletes and privacy.
System DesignOnsite - MetaMediumSoftware EngineerDesign a Real-Time Chat Service for One-to-One and Group Messaging
A system design question on a real-time chat service with one-to-one and group conversations, instant delivery to online users, offline catch-up, and history across devices. It tests connection routing, per-conversation ordering, idempotent sends, message storage, group fan-out, and read receipts.
System DesignOnsite - MetaMediumSecurity EngineerDesign a Real-Time Collaborative Coding Interview Pad, Then Threat-Model It
A two-phase security engineering design prompt from Meta: design a browser-based collaborative coding pad with real-time co-editing, code execution and session playback, then threat-model your own architecture. It tests concurrent editing, sandboxing untrusted code, access control, tenant isolation and risk prioritization.
System DesignOnsite - MetaMediumSoftware EngineerDesign a Real-Time Trending Hashtags System
This question evaluates a candidate's ability to design a large-scale, real-time data processing system that continuously scores and ranks trending content under strict freshness constraints. It tests system design skills including stream processing, time-decayed scoring, and handling high write throughput at scale. This type of question is commonly used to assess architectural thinking for systems balancing timeliness, popularity, and novelty signals.
System DesignOnsite - MetaMediumSoftware EngineerDesign a Reliable Notification System
Design an enterprise-scale notification platform supporting multi-channel delivery, recipient fanout, preference filtering, and resilient at-least-once delivery guarantees.
System DesignOnsiteDistributed SystemsMessage QueuesScalability - MetaHardData ScientistDesign a Restaurant Recommendation System for Food Apps
Meta machine learning system-design prompt on restaurant recommendations for food apps, covering retrieval and ranking models, features, cold start, implicit feedback, offline metrics, A/B testing, and marketplace guardrails.
Machine LearningOnsite - MetaMediumSoftware EngineerDesign a Top-K trending items service
This question tests the ability to design a high-throughput, distributed system that maintains a real-time ranking of the most popular items across a massive global user base.
System DesignOnsiteDistributed SystemsData StreamsCaching - MetaMediumSoftware EngineerDesign a YouTube/Netflix video platform
This question evaluates a candidate's understanding of distributed systems architecture, media processing and delivery concepts including upload resiliency, transcoding into multiple bitrates, scalable storage and low-latency streaming.
System DesignOnsite - MetaHardSoftware EngineerDesign a content processing service
This question evaluates understanding of designing scalable, reliable content-processing systems that handle high-throughput ingestion, ML-based classification and rule evaluation, priority/backpressure mechanisms, storage and API design, experimentation hooks, and operational concerns like observability and cost.
System DesignOnsite - MetaHardData ScientistDesign a feed ads A/B test with guardrails
This question evaluates a data scientist's competency in experimental design, causal inference, statistical power analysis, and product-metric trade-offs within the Analytics & Experimentation domain, and is commonly asked to assess the ability to increase monetization while protecting user and creator experience.
Analytics & ExperimentationOnsite
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