Meta interview questions

Showing 241–270 of 666 results

• Design a scalable banking systemHard

  This problem tests your ability to design a distributed, high-concurrency ledger system that maintains strict consistency and atomicity for financial transactions.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design a small-sample launch experiment in EuropeHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design a ticketing purchase systemHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a weapon-ad harmful content detection systemMedium

  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.

  MetaMachine Learning EngineerSystem DesignTechnical Screen
• Design a weapon-sale ad detection systemMedium

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design ad click tracking and dashboard at scaleMedium

  This problem tests your ability to design a high-throughput, fault-tolerant ingestion pipeline and an aggregation service for real-time analytics dashboarding.

  MetaSoftware EngineerSystem DesignOnsite
• Design an A/B Test for Group Video Calls ImpactHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design an A/B test for pinned-unread featureHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design an Automated Ticket Investigation AgentHard

  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.

  MetaSoftware EngineerML System DesignTechnical Screen
• Design an Expected O(1) Randomized ContainerMedium

  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.

  MetaSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design an Experiment to Evaluate New ML ModelHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design an Experiment to Evaluate New Recommendation ModelHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design an Instagram-like photo-sharing platformHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design an LLM-Based Coding AssistantHard

  Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an LLM-Based Conversational Assistant (Chatbot)Hard

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an On-Demand Delivery PlatformMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design an Online Game LeaderboardHard

  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.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design an ad click tracking systemHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design an ad recommendation and ranking systemMedium

  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.

  MetaData ScientistMachine LearningOnsite
• Design an experiment to evaluate a new ads algorithmHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design an image copyright-violation detection systemMedium

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design an in-memory cloud file systemMedium

  This question evaluates understanding of object-oriented design, stateful data structures, and resource management including user quotas, ownership semantics, and file metadata operations.

  MetaSoftware EngineerCoding & AlgorithmsOnline Assessment
• Design an in-memory cloud storage systemHard

  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.

  MetaSoftware EngineerSystem DesignOnline Assessment
• Design an in-memory database with TTL and historyMedium

  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.

  MetaSoftware EngineerCoding & AlgorithmsOnline Assessment
• Design an online auction systemMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design an online chess game with undo and leaderboardMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design an online coding judgeHard

  This problem tests the ability to architect a distributed, multi-tenant system for sandboxed execution of untrusted code, emphasizing security, scalability, and performance isolation.

  MetaSoftware EngineerSystem DesignOnsite
• Design an online coding platformMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design analytics and experiment for group video callsHard

  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.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design and Evaluate a Solver for a 36-Card Sum-Fifteen GameHard

  Tests state-space search (DFS with memoization), heuristic design, and simulation benchmarking for a deterministic 36-card solitaire game.

  MetaSoftware EngineerSoftware Engineering FundamentalsOnsite