Meta interview questions

Showing 211–240 of 666 results

• Design a Restaurant Recommendation System for Food AppsHard

  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.

  MetaData ScientistMachine LearningOnsite
• Design a Short-Video Recommendation SystemMedium

  This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Design a String Calculator with Explicit Grammar and Evaluation RulesEasy

  This problem tests the ability to implement a parser for basic mathematical expressions, specifically focusing on handling operator precedence and parentheses using a stack-based approach.

  MetaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design a Top-K trending items serviceMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a Trade Ledger ClassEasy

  This problem evaluates your ability to design a class interface that maintains an ordered collection of events and allows efficient retrieval based on time-based criteria.

  MetaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design a YouTube/Netflix video platformMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a bank system with scheduled transfersMedium

  This question evaluates object-oriented design and stateful system skills, including time-ordered event processing, scheduling and cancellation semantics, balance accounting, and top-k aggregation using appropriate data structures.

  MetaSoftware EngineerCoding & AlgorithmsOnline Assessment
• Design a bank system with scheduling and rankingsHard

  Design a bank system with scheduling and rankings 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 DesignTechnical Screen
• Design a bank system with transfersHard

  This question evaluates system-design and transactional engineering skills for financial services, including API design, data modeling, correctness under holds and transfer expirations, idempotency, merge semantics, and complexity reasoning.

  MetaSoftware EngineerSystem DesignHR Screen
• Design a basic bank system APIHard

  This question evaluates a candidate's ability to design and implement an in-memory banking API, focusing on data modeling, API semantics, invariants (such as monotonic timestamps and non-negative balances), idempotency, and basic concurrency control.

  MetaSoftware EngineerSystem DesignOnline Assessment
• Design a clustered A/B test with spilloversHard

  This question tests the ability to design a cluster-randomized experiment when interactions between users (network interference) violate the stable unit treatment value assumption (SUTVA).

  MetaData ScientistAnalytics & ExperimentationHR Screen
• Design a clustered notification experiment with guardrailsMedium

  This question evaluates experimental-design and causal-inference competencies for cluster-randomized experiments, covering metric selection, power and design-effect calculations, intracluster correlation, spillover detection, sequential monitoring, guardrails, and operational rollout within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to determine how applicants balance statistical rigor and real-world operational constraints—such as cluster choice, monitoring thresholds, and analysis strategy—testing both conceptual understanding and practical application in production experimentation.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design a content processing serviceHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a feed ads A/B test with guardrailsHard

  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.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design a fitness tracking appMedium

  This problem tests your ability to design a scalable backend for a fitness tracking application, focusing on high-frequency sensor data ingestion, geospatial route storage, and efficient retrieval of historical user activities.

  MetaSoftware EngineerSystem DesignOnline Assessment
• Design a flight search platformMedium

  This question tests the ability to design a high-scale search platform that aggregates volatile data from heterogeneous third-party providers while maintaining low latency and high availability.

  MetaSoftware EngineerSystem DesignOnsite
• Design a game leaderboard serviceMedium

  This question tests the ability to design a high-throughput, low-latency leaderboard system, focusing on data structures for ranking, storage trade-offs, and scalability under heavy write and read loads.

  MetaSoftware EngineerSystem DesignOnsite
• Design a large-scale web crawlerMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a live video comments systemMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a marketplace price tracking serviceMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a mini banking systemMedium

  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.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design a newsfeed dislike modelMedium

  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.

  MetaMachine Learning EngineerML System DesignOnsite
• Design a post privacy/visibility systemMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a price drop tracking serviceMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a product notifications systemHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a ranking and recommendation systemMedium

  This problem tests your ability to design a multi-stage recommendation pipeline that balances low-latency retrieval with high-precision ranking at scale.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design a real-time ad impression aggregatorMedium

  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.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design a real-time messengerHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design a recommendation systemHard

  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.

  MetaMachine Learning EngineerSystem DesignOnsite
• Design a scalable MoE pretraining pipelineHard

  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.

  MetaMachine Learning EngineerML System DesignOnsite