Meta interview questions

Showing 421–450 of 666 results

• Handle priority conflicts, setbacks, and initiativesMedium

  This question evaluates interpersonal and leadership competencies—communication and influence, structured decision-making and tradeoff reasoning, ownership and accountability, a learning mindset, and the ability to execute through ambiguity and cross-functional constraints.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Handle sales pressure with analytical integrityMedium

  This question evaluates a data scientist's skills in analytical integrity, causal inference, experimental design, stakeholder communication, and change management when pressured to convert a correlation into policy.

  MetaData ScientistBehavioral & LeadershipTechnical Screen
• Hiking App: Design, Metrics, and Go-to-MarketMedium

  Practice designing a hiking mobile app with target users, value proposition, MVP features, roadmap, offline maps, safety trade-offs, metrics, instrumentation, launch strategy, partnerships, growth loops, and monetization options.

  MetaProduct ManagerProduct / Decision MakingOnsite
• How investigate a brand-ad spend drop?Medium

  This question evaluates a data scientist's competency in diagnosing ad-spend anomalies, including data validation, attribution and reporting integrity, segmentation and placement effects, and causal inference within advertising analytics.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How to Validate Friends' Content Engagement Hypothesis?Medium

  Evaluates how to validate whether friends' content drives stronger social engagement than unconnected content. Strong answers define socialness metrics, control observational bias, test launch impact for unconnected feed content, and monitor discovery, retention, cannibalization, and quality guardrails.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How to answer Staff (L6) behavioral interview questionsMedium

  These behavioral prompts evaluate leadership competencies including setting technical direction, stakeholder management, influence without formal authority, trade-off analysis, and mentoring at an organization-wide (Staff/L6) level within the Behavioral & Leadership domain for senior software engineers.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• How to design Shop ad rankingHard

  This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.

  MetaData ScientistMachine LearningTechnical Screen
• How to evaluate a similar-listing notifications featureEasy

  A Meta Data Scientist analytics-and-experimentation interview question: a US C2C second-hand marketplace is considering opt-in “similar listings” notifications. You must (1) decide whether to build it — hypotheses, opportunity sizing, risks, MVP plan — and (2) design the post-launch measurement: a user-randomized A/B test with a primary/diagnostic/guardrail metric stack, handling of opt-in selection bias, and a clear launch/rollback rule.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How to evaluate new listing notifications?Medium

  This problem tests your ability to design an A/B test for a marketplace notification feature, focusing on balancing buyer engagement with potential cannibalization and ecosystem health metrics.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How would you define and use retention metrics?Easy

  This question evaluates a data scientist's competency in product analytics and experimentation by assessing understanding of retention metric definitions, cohorting strategies, short- versus long-term trade-offs, common measurement pitfalls, and A/B test design considerations.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How would you design a Shop Ads ranking algorithm?Easy

  This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.

  MetaData ScientistMachine LearningTechnical Screen
• How would you drive product growth?Medium

  This question evaluates product growth analytics competencies including experimentation design, funnel decomposition, metric definition and guardrails, segmentation, and causal inference within the Analytics & Experimentation domain for social media and mobile product features.

  MetaProduct AnalystAnalytics & ExperimentationOnsite
• How would you evaluate emoji reactions launch?Easy

  This question tests the ability to design an experimentation framework for a new feature in a social product, specifically focusing on metric selection, identifying interference, and isolating the causal impact of a feature rollout.

  MetaData ScientistAnalytics & ExperimentationOnsite
• How would you evaluate pixel-issue notifications?Easy

  This question tests the ability to build a comprehensive evaluation framework for a new feature, specifically focusing on defining success metrics, identifying potential side effects, and designing an experimental setup for pixel-issue notifications.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How would you evaluate upranking shop ads?Easy

  This question tests your ability to design an A/B testing framework for evaluating ad ranking changes, including metric selection, segment analysis, and identifying potential biases in historical data.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How would you grow key product metrics?Easy

  This question assesses your ability to systematically approach product growth by identifying bottlenecks, framing hypotheses, and designing experiments to improve key user engagement metrics.

  MetaProduct AnalystAnalytics & ExperimentationOnsite
• How would you measure Group Call success?Medium

  This question evaluates a data scientist's ability to design a measurement framework encompassing product goal definition, north-star metric selection, success metrics for adoption, engagement and quality, precise retention definitions (app-level vs feature-specific and creator vs participant), cohort retention windows, and experimental design including guardrails, bias sources and network effects. It is asked in the Analytics & Experimentation domain to assess practical application of product analytics and quantitative reasoning about short-term versus long-term tradeoffs, segmentation and experimental validity, operating at a primarily practical application level with necessary conceptual understanding.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• How would you predict a car’s turning intention?Medium

  This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).

  MetaData ScientistMachine LearningOnsite
• Hyperlink Request Flow at FacebookMedium

  Practice explaining the end-to-end hyperlink request flow in Facebook, covering mobile client events, routing, DNS, TLS, gateways, auth, GraphQL, caching, rendering, external safe-link handling, performance, security, privacy, and observability.

  MetaProduct ManagerProduct / Decision MakingOnsite
• Identify 3-Person Call Cycles in Video-Calling AppMedium

  This question evaluates SQL data-manipulation competency, including aggregation, distinct counting, date filtering and relational pattern detection (three-node cycles) to measure user engagement and group-call signals.

  MetaData ScientistData Manipulation (SQL/Python)Technical Screen
• Identify Algorithms for Detecting Malicious Duplicated ContentMedium

  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 Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistMachine LearningTechnical Screen
• Identify Features for Fake News Detection on FacebookHard

  Identify Features for Fake News Detection on Facebook evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes 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 ScientistMachine LearningOnsite
• Identify Metrics to Detect Fake-Account Activity on FacebookMedium

  This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Identify Metrics to Detect Fake-Account Activity on Facebook states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistStatistics & MathOnsite
• Identify Top 10 Users by Average Call DurationMedium

  This question evaluates SQL-based data manipulation and time-series aggregation skills, specifically computing per-user metrics from timestamped event data such as call durations.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Identify non-table data for feature demandHard

  The question evaluates a data scientist's competency in triangulating product demand from non-table sources, defining and de-biasing signals, specifying metrics and guardrails, and designing evidence-driven experiments within the Analytics & Experimentation domain for a Data Scientist role.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Impact and Conflict Stories for a Level-Calibrating Behavioral InterviewMedium

  A behavioral interview asking for a story about your most impactful work and a story about a significant conflict, in a round that weighs heavily in level calibration. It tests scope, ownership, measurable results, handling disagreement constructively, and how well stories hold up under follow-up probing.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Implement 1NN Embeddings and Forward PassHard

  Implement a fully vectorized forward pass that projects input features into an embedding space and classifies them using a 1-nearest-neighbor lookup against a reference bank.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Implement BST Iterator and Ticket QueueMedium

  This problem tests your ability to implement an iterator for a Binary Search Tree (BST) using controlled traversal, and your skill in designing a priority-based queue that handles multi-criteria ordering.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement K-Means Clustering From ScratchMedium

  An ML coding question that asks for a from-scratch implementation of k-means clustering with no provided test cases. It tests the assignment and update steps, vectorized distance computation, initialization, stopping rules, empty-cluster handling and the ability to verify code with self-constructed checks.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Implement LRU cache and copy random listMedium

  This problem tests your ability to handle complex pointer manipulation through linked-list deep copying and your proficiency in designing a thread-safe-capable cache using hash maps and doubly-linked lists.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite