practice a little every day, paws crossed ♡
Software Engineer 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
- Companies
- 11
- Topics
- 8
- Languages
Showing 3181–3210 of 5874 results
- 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - MetaHardSoftware EngineerDesign place-of-interest ML system
Design place-of-interest ML system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign prefix-sum function and max stack
This question evaluates understanding of prefix-sum precomputation for constant-time range queries and the design of a max-enabled stack, focusing on data structure design, algorithmic complexity, and space-time trade-offs.
Coding & AlgorithmsTechnical Screen - MetaMediumSoftware EngineerDesign real-time live comments
This question evaluates the candidate's ability to design scalable, low‑latency real‑time messaging systems, focusing on high‑fanout delivery, approximate time ordering, moderation and rate‑limiting mechanisms, and replay of recent events.
System DesignOnsite - MetaHardSoftware EngineerDesign searchable status & geolocation services
Design searchable status & geolocation services 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
Meta