Meta interview questions

Showing 301–330 of 666 results

• Design weapon-selling ad detection from postsMedium

  This question tests the ability to design an end-to-end multimodal classification system for detecting policy-violating content (weapon sales) while balancing latency, precision, and robustness against adversarial evasion.

  MetaMachine Learning EngineerML System DesignOnsite
• Detect a Contiguous Subarray with a Target SumMedium

  Determine whether an integer array containing negative values has any nonempty contiguous subarray that equals a target, using prefix sums for a linear-time solution.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Detect and address Simpson’s paradoxMedium

  This question evaluates understanding of aggregation bias and Simpson's paradox, proficiency with stratified or mixed-model estimators, heterogeneity testing, and experiment decisioning within Analytics & Experimentation for a Data Scientist role.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Detect bots using comment distribution patternsEasy

  This problem tests your ability to define metrics for identifying automated behavior and your statistical intuition for validating those metrics against human behavior.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Detect fake accounts and measure their impactEasy

  This question tests the ability to design an analytical framework for identifying fraudulent behavior and quantifying the financial or engagement impact of those accounts on a platform.

  MetaAnalytics EngineerAnalytics & ExperimentationOnsite
• Detect leakage and evaluate a prediction modelHard

  This question evaluates competence in production-ready supervised machine learning—detecting and repairing data leakage in logs-based features, designing time-aware cross-validation, defining KPI-driven thresholding and calibration, interpreting model coefficients, and establishing monitoring and retraining policies for a churn prediction model in the Machine Learning domain. It is commonly asked because interviewers need to assess both conceptual understanding of leakage, validation, and calibration principles and practical application skills for deploying and maintaining models with cost-sensitive objectives and monitoring in production, so the level of abstraction spans conceptual reasoning and hands-on practical application.

  MetaData ScientistMachine LearningOnsite
• Determine High-Quality Notifications with CTR AnalysisMedium

  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 Determine High-Quality Notifications with CTR Analysis states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Determine Key Metrics and Design A/B Test for Ad RankingHard

  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 Determine Key Metrics and Design A/B Test for Ad Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Determine Metrics to Evaluate Notification Impact on UsersMedium

  Evaluates product analytics judgment for Facebook push notifications, including how to measure helpful versus harmful notifications, design an event-notification experiment, and balance short-term clicks against retention, fatigue, and opt-out guardrails. Strong answers build a metric hierarchy and use causal measurement.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Determine Old vs. New Users' Shop Visibility ChangesMedium

  This question evaluates SQL-focused data manipulation skills and analytical competency in cohort definition and metric selection for comparing behavioral propensities, specifically assessing how to measure the likelihood of old versus new users switching a shop profile to invisible.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Determine Posterior Probability of Bad User PredictionEasy

  Meta Bayes theorem question on a bad-actor classifier, covering prevalence, sensitivity, specificity, false positives, positive predictive value, and the base-rate effect.

  MetaData ScientistStatistics & MathOnsite
• Determine Probability of Fourth Good Response After Three SuccessesMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Determine Probability of Fourth Good Response After Three Successes states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistStatistics & MathOnsite
• Determine Probability of Friend Request Being FakeEasy

  Determine Probability of Friend Request Being Fake 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
• Determine Unhealthy Oculus Usage with SQL AnalysisMedium

  This question evaluates SQL-based data manipulation and time-series analysis competencies, including sessionization, user-day aggregation, and detection of sustained behavioral patterns in event data; it is categorized under Data Manipulation (SQL/Python) and targets practical application skills.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Determine Value of Prioritizing Accounts by Unread NotificationsMedium

  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 Determine Value of Prioritizing Accounts by Unread Notifications states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Determine feasibility and clean parentheses stringMedium

  This problem tests your ability to manipulate string structures using stack-based logic and perform graph-based dependency resolution using topological sort.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Determine if subarray sums to targetMedium

  This question evaluates a candidate's ability to reason about contiguous subarray sums, efficient array manipulation, and handling large numeric ranges within algorithmic constraints, and it falls under the Coding & Algorithms domain as a practical algorithmic implementation problem.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Determining the optimal ad load in News FeedHard

  Evaluates optimal News Feed ad-load strategy using marginal revenue, user tolerance, and long-term LTV. Strong answers test ad-load levels, segment tolerance, personalize caps, and monitor revenue, retention, and user-experience guardrails.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Diagnose a Single-Server Crash with Concrete Linux EvidenceMedium

  Walk through a single-server crash investigation using Linux service, kernel, memory, disk, and boot evidence while planning safe recovery.

  MetaSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Diagnose sales correlations without claiming causalityHard

  This question tests the ability to design an observational study that identifies associations between a new feature adoption and sales growth while strictly avoiding causal claims in the presence of self-selection bias.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Discuss Projects, Failures, and GrowthHard

  This question evaluates interpersonal and leadership competencies—communication, teamwork, conflict resolution, accountability, resilience, and growth mindset—relevant to a Machine Learning Engineer and is categorized under Behavioral & Leadership.

  MetaMachine Learning EngineerBehavioral & LeadershipOnsite
• Discuss Research Experience and ChallengesMedium

  This question evaluates research design, experimental methodology, problem formulation, ownership, collaboration, prioritization, and the ability to learn from failed or ambiguous outcomes within the Machine Learning Engineer role, encompassing communication and leadership competencies.

  MetaMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Discuss a Disagreement with a TeammateMedium

  This question assesses the candidate's ability to navigate professional conflict, prioritize team goals over personal ego, and maintain effective working relationships through data-driven communication.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Discuss achievements, ambiguity handling, growth, and conflict managementMedium

  Discuss achievements, ambiguity handling, growth, and conflict management evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes 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 EngineerBehavioral & LeadershipOnsite
• Discuss project pride, conflict, and pivotsMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss project pride, conflict, and pivots states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Employ Collaborative Filtering for Personalized Recommendation ListsMedium

  Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.

  MetaData ScientistMachine LearningTechnical Screen
• Engineering Manager Behavioral: Spotting and Closing a Skill Gap on Your TeamMedium

  An engineering manager behavioral question about realizing that the team lacked a skill it needed. It tests how the gap was noticed and assessed, the trade-offs among training, hiring and borrowing expertise, protecting delivery while people learn, and how the closing of the gap was measured.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Engineering Manager Behavioral: Struggling Engineers and Sustained UnderperformanceMedium

  A two-part engineering manager behavioral question: helping a team member who was struggling, and handling sustained underperformance. It tests early detection, diagnosing causes with the person, tailored support, specific written expectations, a fair process with HR, and a decisive, respectful outcome.

  MetaSoftware EngineerBehavioral & LeadershipTechnical Screen
• Ensure Effective Teamwork Amid Conflicting Stakeholder OpinionsMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Ensure Effective Teamwork Amid Conflicting Stakeholder Opinions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistBehavioral & LeadershipOnsite
• Estimate CTR lift with binomial tests and errorsHard

  This question evaluates statistical inference for proportions, sequential monitoring, and multiple-comparison error control—assessing competence with binomial-based CTR comparison, pooled standard errors, z-tests and confidence intervals, Type I error inflation from peeking, and FWER/FDR considerations.

  MetaData ScientistStatistics & MathOnsite