Meta interview questions

Showing 271–300 of 666 results

• Design and analyze a group-calls experimentMedium

  This question evaluates competency in experimental design, causal inference, metric specification, interference handling, and infrastructure-aware analytics within the Analytics & Experimentation domain for a Data Scientist role.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design and evaluate P2P payments in messagingHard

  This question evaluates a data scientist's competency in product analytics, causal inference, experimental design under network interference, monetization measurement (ARPU impact), and risk mitigation for a peer-to-peer payments feature relevant to a Data Scientist role.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design and evaluate an ads ranking algorithmEasy

  This problem tests your understanding of the end-to-end machine learning lifecycle for an ads ranking system, focusing on feature engineering, model selection, and the transition from offline metrics to online business impact.

  MetaAnalytics EngineerMachine LearningOnsite
• Design and validate ad model launchMedium

  This question evaluates competence in experimental design, causal inference, online A/B testing and launch decision frameworks for ad recommendation systems, covering randomization/unit choice, metric definition and guardrails, power and duration estimation, validity checks, monitoring and visualization in the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design and validate an ads feed experimentHard

  This question tests the ability to design a rigorous A/B test for a core product feature, covering metric selection, interference handling, and post-experiment analysis under complex constraints.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design batch and streaming ETL architectureHard

  Design batch and streaming ETL architecture 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.

  MetaData EngineerSystem DesignOnsite
• Design cluster-randomized test under network effectsHard

  This question evaluates a data scientist's experiment design and causal inference skills under interference, covering exposure and estimand definition, graph-based cluster construction, cluster-level randomization and contamination mitigation, metric selection, and design-effect/sample-size calculations.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design coding platform with global leaderboardMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design comment ranking for a news feedMedium

  This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.

  MetaMachine Learning EngineerML System DesignOnsite
• Design experiment for Group Calls with interferenceHard

  This question evaluates a data scientist's competency in experimental design and causal inference for networked features, covering metrics selection (primary and guardrail metrics), strategies for mitigating interference via randomization or quasi-experimental approaches, exposure and contamination controls, rollout planning, and pre-registration and power analysis. Commonly asked in Analytics & Experimentation interviews for Data Scientist roles, it assesses reasoning about bias–variance trade-offs, engineering constraints, and validity diagnostics in interconnected user environments, requiring both conceptual understanding of identification and practical application of experiment implementation.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design experiments and diagnose metric changesEasy

  This question evaluates product analytics and experimentation skills, including experiment design, metric framework definition, causal reasoning, diagnostic analysis, and marketplace operations trade-offs within the Analytics & Experimentation domain.

  MetaProduct AnalystAnalytics & ExperimentationTechnical Screen
• Design experiments under network interferenceHard

  This question evaluates a data scientist's competence in experimental design and causal inference under network interference, assessing understanding of clustering strategies, exposure and exclusion definitions, contamination control, design effect and sample-size implications, estimation of intra-cluster correlation, and mixed-effects analysis for evolving clusters. Commonly asked in Analytics & Experimentation interviews to probe the ability to preserve internal validity while managing operational feasibility in two-sided marketplaces, it tests both conceptual understanding of interference and practical application skills in design and analysis planning.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design image and multimodal generation systemsHard

  This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Design input validation and error handlingHard

  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.

  MetaSoftware EngineerSystem DesignTechnical Screen
• Design marketplace experiments at DoorDashMedium

  This question evaluates a candidate's skills in experimental design, product and marketplace analytics, causal inference, and diagnostic data science for multi-sided platforms.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design metrics for violating content exposureHard

  This question evaluates competency in metric design and measurement for content safety, including defining exposure metrics, inclusion/exclusion rules, handling label latency and appeals, uncertainty-aware alerting, and A/B test design for a Data Scientist role in the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design metrics to detect harmful content and fraudEasy

  This problem tests the ability to design robust monitoring metrics for detecting fraudulent user activity and policy-violating content, focusing on trade-offs between precision and recall.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Design nearby place recommendationsHard

  This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.

  MetaMachine Learning EngineerML System DesignOnsite
• Design place-of-interest ML systemHard

  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.

  MetaSoftware EngineerML System DesignOnsite
• Design pre-launch plan and cluster A/B testMedium

  This question evaluates a candidate's skills in experimental design, causal inference, cluster randomization, metric definition and instrumentation, power analysis, and phased rollout decision-making.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Design prefix-sum function and max stackMedium

  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.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design real-time live commentsMedium

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design robust group size limiting for callsMedium

  This question evaluates distributed systems design, concurrency control, admission-control algorithms, and scalability under real-time and race-condition constraints.

  MetaData ScientistCoding & AlgorithmsTechnical Screen
• Design scalable media storage and deliveryHard

  This question evaluates an engineer's ability to design globally distributed, highly scalable media storage and delivery systems, covering competencies in object storage and metadata modeling, CDN integration, replication and consistency strategies, security and access control, background processing, observability, quotas, and cost/performance trade-offs. It is commonly asked in system design interviews to assess reasoning about durability, availability, latency and cost trade-offs while requiring both high-level architectural thinking and practical implementation details; category: System Design; level of abstraction: combines conceptual architecture with practical application considerations.

  MetaMachine Learning EngineerSystem DesignOnsite
• Design searchable status & geolocation servicesHard

  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.

  MetaSoftware EngineerSystem DesignOnsite
• Design structure for top-K frequent elementsMedium

  This question evaluates a candidate's competency in algorithm and data-structure design, frequency counting and streaming/approximation techniques, and the ability to reason about time/space complexity and workload-driven trade-offs.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Design tables for event-driven metricsHard

  This question evaluates relational data modeling and data engineering competencies for high-volume event analytics, including schema design, partitioning and clustering strategies, duplicate and late-arriving event handling, and normalization versus denormalization trade-offs.

  MetaData EngineerSystem DesignTechnical Screen
• Design the Frontend Data Flow and APIs for a Social Media News FeedMedium

  A product architecture design problem: starting from a feed mock-up, design the client data flow and APIs for a social news feed on web and mobile. It tests UX flow analysis, the shape of the feed response object, cursor-based pagination, and edge cases across client types.

  MetaSoftware EngineerSystem DesignOnsite
• Design ticketing system with seat holdMedium

  This question evaluates a candidate's skills in system design, distributed systems, concurrency control, data modeling, and scalability, testing their ability to reason about correctness and performance under high contention and heavy traffic as well as trade-offs between consistency and availability.

  MetaSoftware EngineerSystem DesignOnsite
• Design video-ads experiment and handle null resultsMedium

  This question evaluates experimental design, causal inference, statistical power analysis, metric definition, and operational analytics competency within the Analytics & Experimentation domain for a Data Scientist role.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen