Meta interview questions

Showing 181–210 of 666 results

• Design A/B test and success metrics for new featureHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design B2C chatbot success metrics and test planMedium

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design Boolean Search for Status PostsHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design Boolean Search over User Status PostsMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design Experiment to Measure Shopping Feature ImpactHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design LCA and find K closest pointsMedium

  This question evaluates understanding of tree algorithms (LCA using parent pointers) and selection algorithms (sorting, max-heap, quickselect), emphasizing data structures, algorithmic correctness, and time/space complexity within the Coding & Algorithms domain.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design Machine Learning Model for Facebook Groups Post RankingHard

  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.

  MetaData ScientistMachine LearningOnsite
• Design Messenger spam experiment with clusteringHard

  This question evaluates a data scientist's skills in experimental design, causal inference, clustering and interference analysis, metric specification, and power/sample-size calculation within the Analytics & Experimentation domain for Data Scientist roles.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design Metrics to Track and Analyze Spam ImpactMedium

  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 Metrics to Track and Analyze Spam Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design Nearby and Notification RankingHard

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design Place Recommendation SystemMedium

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design Real-Time Auctions for Social PostsMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design Ride Requests, Driver Matching, and Trip StateHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design SQL Query for Shop Visibility and User Activity MetricsMedium

  This question evaluates proficiency in SQL-based data manipulation—aggregation, JOINs, CASE expressions, window functions, ranking, and metric design for user tenure segmentation and temporal activity comparisons.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Design Sampling Strategy to Estimate Fake News ProportionMedium

  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.

  MetaData ScientistStatistics & MathOnsite
• Design Search And Web Crawling SystemsMedium

  This question tests the ability to design a scalable web crawler capable of handling billions of pages while maintaining freshness and avoiding redundant work.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design a Code Review AgentMedium

  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.

  MetaSoftware EngineerML System DesignOnsite
• Design a Concurrent, Memory-Bounded Tally ServiceMedium

  This problem tests the ability to design a high-throughput, memory-efficient data structure for time-series counting that balances concurrency, accuracy, and storage constraints.

  MetaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design a Dropbox-like file storage and sync serviceHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a GPS Fitness Tracking App with Activity Uploads and a Friends FeedMedium

  A system design interview question asking you to build a GPS fitness-tracking app in the style of Strava, where athletes record runs and rides, save routes and follow friends' activity feeds. It tests offline-tolerant recording, duplicate-free uploads, storage for large GPS tracks, feed fan-out and privacy controls.

  MetaSoftware EngineerSystem DesignOther
• Design a Large-Scale Personalized Recommendation System End to EndMedium

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a Messenger-Style Chat Backend Without WebSockets or Persistent ConnectionsMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Notification System That Delivers Events to Users at ScaleMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a People-You-May-Know Friend Recommendation SystemMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Ranked Home News Feed for a Large Social NetworkMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Real-Time Chat Service for One-to-One and Group MessagingMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Real-Time Collaborative Coding Interview Pad, Then Threat-Model ItMedium

  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.

  MetaSecurity EngineerSystem DesignOnsite
• Design a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and RewardsMedium

  An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Design a Real-Time Trending Hashtags SystemMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Reliable Notification SystemMedium

  Design an enterprise-scale notification platform supporting multi-channel delivery, recipient fanout, preference filtering, and resilient at-least-once delivery guarantees.

  MetaSoftware EngineerSystem DesignOnsite