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Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
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- 11
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- 8
- Languages
Showing 4771–4800 of 9403 results
- MetaMediumData ScientistAnalyze User Transfer Distribution in Initial Launch Period
Evaluates distribution analysis for transfer counts after a new P2P payments launch. Strong answers specify the denominator, describe zero inflation and right skew, locate mean, median, mode, and p95, and explain how adoption, cohorts, and exposure change the distribution over time.
Statistics & MathOnsite - MetaMediumData ScientistAnalyze ad targeting expectations and distributions
This question evaluates a data scientist's competency in probabilistic modeling, expected-value decision-making, variance decomposition for mixture distributions, and Poisson/exponential arrival-process analysis within the Statistics & Math domain.
Statistics & MathHR Screen - MetaMediumData ScientistAnalyze skewed comments and sampling effects
This question evaluates understanding of descriptive statistics for right‑skewed count data and inferential concepts such as sampling distributions, standard error, percentiles, and the Central Limit Theorem, and is commonly asked to probe how skewness and sample size affect central tendency measures and uncertainty in estimates.
Statistics & MathOnsite - MetaMediumData ScientistAnalyzing abuse in the content‑reporting system
Evaluates reporting-system analytics for valid reports and misuse detection in moderation workflows. Strong answers define distinct reported-user share, write SQL for valid reports, and detect abusive reporters through frequency, hit-rate, burst, and coordination metrics.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAnnotating and forecasting a long‑tail distribution
Evaluates robust summarization and forecasting for long-tailed daily share-count distributions. Strong answers compute mean, median, P1, P99, visualize with log scales or percentile bands, and forecast seasonality with heavy-tail-aware methods.
Statistics & MathOnsite - MetaHardSoftware EngineerAnswer Repeated Range Aggregate Queries on a Static BST
Preprocess a sorted array derived from a static BST to answer many inclusive range queries. Use binary-search boundaries and wide prefix sums to return both the sum and count for each range without rescanning the tree.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerAnswer behavioral questions on projects and feedback
This set of behavioral questions evaluates communication, leadership, adaptability, handling ambiguity, feedback exchange, and project ownership within the context of machine learning engineering projects.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerAnswer conflict, ambiguity, feedback, difficult coworker prompts
This question evaluates a candidate's ability to handle conflict with supervisors, clarify ambiguous requirements, provide constructive feedback and mentorship, and manage difficult collaborators.
Behavioral & LeadershipTechnical Screen - MetaMediumMachine Learning EngineerAnswer core behavioral questions using STAR
This question evaluates behavioral and leadership competencies—including communication, teamwork, conflict resolution, time and scope management, and mentorship—within the context of a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerAnswer four string/array/battery coding questions
This set of four problems evaluates algorithmic competencies including ASCII string processing and character classification, array transformation and operation-count reasoning, cost-minimization for enforcing monotonicity under only-increase constraints, and time-based simulation of resource scheduling with recharge cycles.
Coding & AlgorithmsOnsite - MetaHardSoftware EngineerAnswer key behavioral prompts effectively
This set of behavioral prompts evaluates ownership, communication, conflict resolution, adaptability, and the ability to receive and act on feedback, targeting leadership and interpersonal competencies relevant to software engineering.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerAnswer senior-level behavioral interview questions
This question evaluates leadership, decision-making, risk management, stakeholder management, and impact-communication skills for a senior Machine Learning Engineer role within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - MetaMediumData ScientistApply reinforcement learning to product decisions
This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.
Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation - MetaHardData ScientistApply sequential testing without p-hacking
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.
Statistics & MathOnsite - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
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.
ML System DesignOnsite - MetaMediumProduct ManagerArgue the strategic case for Meta to keep backing Horizon Worlds social VR
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.
Product Design & StrategyOther - MetaMediumData ScientistAssess Cultural Fit and Problem-Solving in Reality Labs Role
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.
Behavioral & LeadershipOnsite - MetaMediumData ScientistAssess ranking change and design experiment
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.
Analytics & ExperimentationTechnical Screen - MetaMediumData ScientistAssessing whether a new metric A is meaningful for News Feed
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.
Analytics & ExperimentationTechnical Screen - MetaMediumSoftware EngineerBehavioral: Acting on Critical Manager Feedback and a Simplification Beyond Your Depth
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.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerBehavioral: Cross-Functional Disagreement and a Manager Conflict You Got Wrong
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.
Behavioral & LeadershipTechnical Screen - MetaMediumSoftware EngineerBehavioral: Managing Shifting Priorities and Changing Approach Mid-Project
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.
Behavioral & LeadershipOnsite - MetaMediumSoftware EngineerBehavioral: Your Highest-Impact Project and an Innovation You Did Not Plan
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.
Behavioral & LeadershipTechnical Screen - MetaHardData ScientistBrainstorm how to optimize email engagement
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.
Analytics & ExperimentationTechnical Screen - MetaMediumMachine Learning EngineerBuild Friend Recommendations
This problem tests your ability to manipulate graph-like data structures to perform neighbor analysis and efficient set operations to rank potential connections.
Coding & AlgorithmsTechnical ScreenGraphsHash MapsSocial Network Analysis - MetaMediumSoftware EngineerBuild a FastAPI summarization service
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.
Coding & AlgorithmsOnsite - MetaHardMachine Learning EngineerBuild a Friend Recommender
This problem tests your ability to implement a graph-based recommendation algorithm using common neighbors and popularity constraints to rank potential connections.
Coding & AlgorithmsOnsiteGraphSortingRecommendation System - MetaHardSoftware EngineerBuild a Mistral-powered RAG agent
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.
ML System DesignOnsite - MetaHardData ScientistBuild dashboard; diagnose engagement–purchase gap
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.
Analytics & ExperimentationOnsite - MetaMediumMachine Learning EngineerBuild harmful-content text classifier
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.
Machine LearningOnsiteNLPClassificationMLOps
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