Meta interview questions

Showing 511–540 of 666 results

• Model Unique Recipients with Poisson Distribution and Test FitMedium

  Evaluates statistical modeling of unique recipient counts per caller using count distributions. Strong answers compare Poisson, binomial, negative binomial, zero-inflated, and segmented models, then test fit with dispersion checks, likelihood metrics, chi-square tests, and simulation-based diagnostics.

  MetaData ScientistStatistics & MathTechnical Screen
• Model session times and comments with exponential/PoissonMedium

  This question evaluates understanding of memoryless processes and count-data modeling, covering exponential (constant-hazard) distributions for session durations and Poisson approximations for comment counts within survival analysis and count-data modeling in Statistics & Math for a Data Scientist role.

  MetaData ScientistStatistics & MathOnsite
• Move Work Forward Across TeamsMedium

  This question evaluates your ability as a senior engineer to drive cross-functional alignment, manage complex technical dependencies, and deliver impact across multiple teams with competing priorities.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Navigate Stakeholder Conflict During a Compressed Delivery TimelineHard

  Describe a project where a deadline moved earlier and stakeholders disagreed about what should ship. Explain how you exposed the conflict, tested a failing approach, incorporated senior feedback, changed course, and used a concrete result to assess the compromise.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Navigate unknown grid to locate targetMedium

  This question evaluates algorithmic problem-solving and spatial reasoning skills, particularly graph traversal, exploration in unknown environments, state encoding for relative coordinates, and reasoning about correctness.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• North-star and guardrail metrics for generative AI across Meta's appsMedium

  A product manager metrics question on judging whether Meta's generative AI features, such as its in-app assistant, creation tools and ad tools, are succeeding. It tests choosing a north-star metric that reflects real value, supporting and guardrail metrics, and how to show the AI actually caused any change.

  MetaProduct ManagerAnalytics & ExperimentationOther
• Optimize Oculus Data Streaming with Bandwidth ConstraintsMedium

  This question evaluates a candidate's ability in algorithm design and problem decomposition for array segmentation and interval coverage, focusing on reasoning about cumulative constraints for streaming data and geometric interval intersections.

  MetaData ScientistCoding & AlgorithmsOnsite
• Optimize Travel Costs and Generate Rotational Symmetric NumbersMedium

  This question evaluates algorithmic optimization and complexity-analysis skills by combining array-based time-dependent pricing for round-trip cost minimization with combinatorial generation of rotationally symmetric (strobogrammatic) k-digit numbers.

  MetaData ScientistCoding & AlgorithmsTechnical Screen
• Optimize a Fifteen-Sum Card StrategyHard

  This problem tests your ability to optimize a combinatorial search space through memoization and dynamic programming to determine the maximum win rate of a card-based strategy.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Optimize flight costs and find grid pathMedium

  This question evaluates algorithm design and problem-solving skills for array-based cost optimization and grid pathfinding, including selection under ordering constraints, path existence detection and path reconstruction, together with correctness and edge-case reasoning.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Optimize repeated-value vectors and compute exclusive timesMedium

  This question evaluates proficiency in data-structure and algorithmic concepts such as run-length encoding for compressed vectors and interval/nesting reasoning for computing exclusive execution times.

  MetaMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Optimize ribbon piece length by binary searchMedium

  This question evaluates understanding of binary-search-on-answer techniques and monotonic predicates, complexity analysis, and implementation-level handling of integer overflow and extreme edge cases in numeric partitioning, situated in the coding and algorithms domain and emphasizing practical implementation considerations rather than purely theoretical proofs. It is commonly asked because it reveals whether an interviewee can reason about monotonicity and design an O(n log max(lengths)) solution that remains robust under very large k, large element values, and potential integer overflow.

  MetaMachine Learning EngineerCoding & AlgorithmsOnsite
• Palindrome After Deleting at Most One CharacterMedium

  This problem tests your ability to optimize string traversal using a two-pointer approach, specifically handling conditional branch logic when an inconsistency is detected.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Persuade engineers to launch pinned-unread chatsHard

  This question evaluates persuasion, product sense, metrics-driven reasoning, and technical trade-off analysis for a Data Scientist within a Behavioral & Leadership domain, requiring clear problem definition, baseline metrics, impact estimation, and consideration of engineering constraints like ranking complexity, real-time updates, caching, client performance, edge cases, and accessibility. It is commonly asked to assess the ability to influence engineering stakeholders, design incremental rollouts with success metrics and guardrails, and balance high-level conceptual strategy with practical implementation planning (both conceptual understanding and practical application).

  MetaData ScientistBehavioral & LeadershipTechnical Screen
• Prepare answers for core behavioral questionsMedium

  This question set evaluates interpersonal and leadership competencies such as communication, conflict resolution, giving and receiving feedback, problem-solving, ownership, handling ambiguity, and the ability to articulate measurable impact and self-awareness.

  MetaSoftware EngineerBehavioral & LeadershipOnsite
• Prevent Private Code Leakage in Coding AgentsMedium

  This problem tests the ability to design an ML-driven safeguard system that prevents private codebase exposure during the training, fine-tuning, and inference phases of a coding agent.

  MetaMachine Learning EngineerML System DesignTechnical Screen
• Produce asymmetric side views of a binary treeMedium

  Produce asymmetric side views of a binary tree evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details 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 EngineerCoding & AlgorithmsTechnical Screen
• Program Execution Deep DiveMedium

  Practice a Product Manager program execution deep dive covering requirements capture, prioritization, stakeholder conflict, risk mitigation, launch planning, staged rollout, metrics, guardrails, and post-launch learning.

  MetaProduct ManagerProduct / Decision MakingOnsite
• Promote Inclusion and Overcome Barriers in Social-Commerce TeamMedium

  Promote Inclusion and Overcome Barriers in Social-Commerce Team 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.

  MetaData ScientistBehavioral & LeadershipOnsite
• Prove friends outperform unconnected; design metrics, observational analysis, and rollout experimentHard

  A Meta data-scientist technical-screen question on Analytics & Experimentation: using only info_stream_views and post_reactions, prove whether Friend-authored content is 'more social' than Unconnected content. It tests denominator-complete metric design with relationship attribution, observational causal validation (fixed effects + propensity/AIPW), and a network-aware rollout experiment with power, CUPED, interference mitigations, and guardrails — plus quantifying the long-term and discovery value of Unconnected content beyond near-term engagement.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Prove high-quality pixels improve ad performanceHard

  This question tests your ability to design a rigorous causal experiment to validate a product hypothesis while accounting for selection bias, confounding variables, and operational constraints.

  MetaData ScientistAnalytics & ExperimentationTechnical Screen
• Prove source growth is cannibalization, not incrementalHard

  This question evaluates causal inference and experimental-design competencies in analytics, testing skills in identification strategy, treatment and control definition, specification of difference‑in‑differences or randomized geo experiments, metric construction, power calculations, and robustness checks for attribution and cannibalization analysis. It is commonly asked to determine whether observed growth is incremental or substitution-driven, falls under the Analytics & Experimentation domain in Data Science, and emphasizes practical application accompanied by conceptual understanding of identification assumptions, statistical power, and measurement.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Rank Ads by Conversion Rate for Top 10 PerformersMedium

  This question evaluates data manipulation and analytics skills for computing time-windowed ad performance metrics, including aggregations of impressions and revenue, join logic between impression and conversion records, conversion-rate calculation, and ranking.

  MetaData ScientistData Manipulation (SQL/Python)Onsite
• Read Current and Historical Field Values with ExpirationMedium

  Design an in-memory key-field-value store that supports historical queries, time-to-live (TTL) expiration, and field deletions without corrupting past states.

  MetaMachine Learning EngineerCoding & AlgorithmsOnline Assessment
• Recommend two-hop follows in PythonMedium

  This question evaluates fluency with graph traversal, set operations, deduplication, and frequency-based ranking in Python, targeting manipulation of directed "follows" relationships represented as dictionaries and is categorized under Data Manipulation (SQL/Python).

  MetaData EngineerData Manipulation (SQL/Python)Onsite
• Redesign an executive dashboard for C-suiteMedium

  This question evaluates skills in dashboard design, data visualization, metric selection, and executive-level analytical communication for product and growth measurement.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Reduce variance with covariate adjustmentMedium

  This question evaluates a data scientist's competency in experimental design, variance reduction techniques, and regression-based covariate adjustment (CUPED/ANCOVA) within randomized A/B tests, including understanding of statistical power and diagnostic checks.

  MetaData ScientistAnalytics & ExperimentationOnsite
• Reflect on Conflict Resolution and Key LearningsMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Reflect on Conflict Resolution and Key Learnings states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  MetaData ScientistBehavioral & LeadershipOnsite
• Remove minimum invalid mixed bracketsHard

  This Coding & Algorithms problem evaluates string-processing and bracket-matching skills, specifically understanding of stack-based parsing, handling multiple bracket types, and minimizing removals while preserving non-bracket characters.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen
• Remove minimum parentheses to balance stringMedium

  This question evaluates string-processing and algorithm-design skills within the Coding & Algorithms domain, focusing on balancing parentheses, correctness proofs for minimal edits, and time/space complexity analysis.

  MetaSoftware EngineerCoding & AlgorithmsTechnical Screen