Meta interview questions

Showing 31–60 of 666 results

• Apply sequential testing without p-hackingHard

  This question evaluates a candidate's competency in sequential hypothesis testing, alpha‑spending rules (Pocock vs O’Brien–Fleming), always‑valid inference methods such as mixture‑SPRT and e‑values, and strategies for controlling error rates across multiple metrics.

  MetaData ScientistStatistics & MathOnsite
• Architect an asynchronous RL post-training systemHard

  This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.

  MetaMachine Learning EngineerML System DesignOnsite
• Argue the strategic case for Meta to keep backing Horizon Worlds social VRMedium

  A product strategy question for a Meta product manager: make the case for why the company should keep investing in Horizon Worlds, its social virtual-reality app. It tests platform and network-effect reasoning, candor about the product's weaknesses and costs, and the ability to define milestones that would justify or end further funding.

  MetaProduct ManagerProduct Design & StrategyOther
• Assess Cultural Fit and Problem-Solving in Reality Labs RoleMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Assess Cultural Fit and Problem-Solving in Reality Labs Role states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistBehavioral & LeadershipOnsite
• Assess ranking change and design experimentMedium

  This question evaluates a candidate's competency in causal inference, experimentation design, metric definition, and observational analytics for product ranking and engagement problems within the Analytics & Experimentation domain for Data Scientist roles.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Assessing whether a new metric A is meaningful for News FeedMedium

  Evaluates whether a proposed News Feed proxy metric is meaningful for long-term user value. Strong answers test predictive power, actionability, gaming risk, true-outcome alignment, collection cost, privacy, latency, and decision criteria.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Behavioral: Acting on Critical Manager Feedback and a Simplification Beyond Your DepthMedium

  A senior behavioral question on growth: explain how you handled critical feedback from your manager, and a time you tried to simplify something before you understood it well enough. It tests self-awareness, learning speed and how you turn a misstep into lasting changes in your habits.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Behavioral: Cross-Functional Disagreement and a Manager Conflict You Got WrongMedium

  A senior behavioral question on conflict: describe a significant disagreement with a partner from another function and a conflict with your manager in which you turned out to be wrong. It tests how you raise disagreement, argue from evidence, change your mind gracefully and protect working relationships.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Behavioral: Managing Shifting Priorities and Changing Approach Mid-ProjectMedium

  A senior behavioral question on prioritization and adaptability: how you manage several competing priorities, whether you prefer shifting or steady work, and a project where you had to change approach midway. It probes trade-off reasoning, stakeholder communication and recognizing when to change course.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Behavioral: Your Highest-Impact Project and an Innovation You Did Not PlanMedium

  A senior behavioral question pairing two prompts: walk through the most impactful project you have worked on, and a time you came up with an innovation you had not set out to create. It probes scope beyond your own team, ownership of ambiguous problems, measurable outcomes and what the experience changed in how you work.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Brainstorm how to optimize email engagementHard

  This question evaluates a data scientist's competency in lifecycle email optimization, attribution of incremental on-site engagement, experiment design, metric selection, and trade-offs involving deliverability and guardrails within the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Build Friend RecommendationsMedium

  This problem tests your ability to manipulate graph-like data structures to perform neighbor analysis and efficient set operations to rank potential connections.

  MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Build a FastAPI summarization serviceMedium

  This question evaluates web API development and backend engineering competencies, including FastAPI usage, Python type hints, Pydantic-based request/response validation, error handling, in-memory state management, and modular router/service/model layering, and it belongs to the Coding & Algorithms category with a focus on backend web service and API design. It is commonly asked to assess practical ability to deliver production-ready REST endpoints with proper validation and error handling under constrained resources, and the level of abstraction required is primarily practical implementation with attention to architectural design.

  MetaSoftware EngineerCoding & AlgorithmsOnsite
• Build a Friend RecommenderHard

  This problem tests your ability to implement a graph-based recommendation algorithm using common neighbors and popularity constraints to rank potential connections.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Build a Mistral-powered RAG agentHard

  This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.

  MetaSoftware EngineerML System DesignOnsite
• Build dashboard; diagnose engagement–purchase gapHard

  This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Build harmful-content text classifierMedium

  This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.

  MetaMachine Learning EngineerMachine LearningOnsite
• Calculate Ad Insertion Statistics for Two MethodsMedium

  Meta statistics prompt comparing two ad-insertion strategies, covering binomial moments, deterministic quotas, tail probability for excessive ads, adjacent ad pairs, and risk of long ad runs.

  MetaData ScientistStatistics & MathOnsite
• Calculate Ad Performance with Click-Through and Conversion RatesMedium

  This question evaluates a candidate's competency in designing and computing ad-performance KPIs by performing data manipulation, joining event tables, applying time-window filters, and aggregating metrics using SQL or Python.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Calculate CTR and ad revenueMedium

  This problem tests your ability to clean and join datasets, handle currency conversion, and aggregate metrics at different granularities.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Daily Survey Response Rates by CountryMedium

  Write a PostgreSQL query to compute daily survey response rates at the country level using distinct user counts while handling orphan response logs and edge cases.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Expected Impressions and Probability for UsersEasy

  Meta probability question on random ad-impression allocation, including binomial expectation, probability a user sees at least one impression, expected unique reach, and large-population approximations.

  MetaData ScientistStatistics & MathOnsite
• Calculate French User Engagement and U.S. Call DurationMedium

  This question evaluates proficiency in SQL-based data manipulation and analytics, testing skills such as joining event and profile tables, aggregating metrics, and computing user-level engagement proportions and average durations.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Posterior Fraud Probability Using Bayes' TheoremEasy

  Calculate Posterior Fraud Probability Using Bayes' Theorem 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
• Calculate Posterior Probability of Flagged User Being Bad ActorEasy

  Evaluates Bayesian inference and confusion-matrix reasoning for abuse detection. Strong answers derive posterior precision from prevalence, TPR, and FPR, define Type I and Type II errors, and compute true positives and false positives for 10 million users.

  MetaData ScientistStatistics & MathOnsite
• Calculate Probability of Honest and Relevant Chatbot AnswersEasy

  Calculate Probability of Honest and Relevant Chatbot Answers 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
• Calculate Survey Response Rate and Quality Metric in SQLMedium

  This question evaluates SQL data-manipulation competencies such as aggregation, NULL handling, deduplication (first-per-group), and computing engagement and quality metrics from event logs.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Total Revenue in USD Using SQL QueryMedium

  This question evaluates proficiency in SQL data manipulation, focusing on joins, aggregations, and handling currency conversion across relational tables.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Calculate Video Call Usage Metrics by Country and DateMedium

  This question evaluates data manipulation and product-analytics skills, focusing on joining user and call datasets, aggregations for distinct-user percentages, and per-DAU duration calculations.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate and Compare Survey Response Rates for User TenureMedium

  This question evaluates proficiency in relational data manipulation and metric computation, including SQL joins, aggregation and cohort-based comparisons relevant to survey response analysis, and is categorized under Data Manipulation (SQL/Python) for a Data Scientist role.

  MetaData ScientistData Manipulation (SQL/Python)Onsite