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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.
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Showing 7141–7170 of 9403 results
- RampHardSoftware EngineerTraverse an HTTP Maze with Retry Handling
This problem tests BFS traversal skills combined with state management for handling intermittent network failures and API constraints.
Coding & AlgorithmsOnsiteBFSGraphAPI - RampHardSoftware EngineerWhat Project Are You Proudest Of?
This question evaluates a candidate's ability to demonstrate ownership, leadership, collaboration, technical contribution, problem-solving, and quantifiable impact from past projects.
Behavioral & LeadershipTechnical Screen - RapidSOSMediumSoftware EngineerDesign the Product Catalog for a Multi-Store Grocery Delivery Marketplace
A senior system design prompt reported from a RapidSOS loop: design the product catalog for a multi-store grocery delivery marketplace like Instacart. It tests modeling shared products versus per-store prices and availability, ingesting messy retailer feeds, store-scoped browse and search, caching, freshness and checkout consistency.
System DesignOther - RbcroyalbankEasyData EngineerDescribe ETL and pipeline challenges
An RBC Data Engineer co-op/intern technical screen combining behavioral and technical prompts: self-introduction, skills from a master's program, handling ETL schema differences, ensuring data integrity, and a STAR story about debugging a pipeline failure. The model answer gives concrete frameworks for schema evolution, multi-stage integrity checks, and root-cause analysis.
Behavioral & LeadershipTechnical Screen - RbcroyalbankEasyData EngineerExplain ETL schema changes and ensure integrity
This question evaluates proficiency in handling ETL schema evolution, maintaining end-to-end data integrity (accuracy, completeness, consistency, timeliness), pipeline reliability, and leadership in incident response, and is commonly asked to gauge experience managing schema drift, enforcing data quality, and owning production pipelines.
Behavioral & LeadershipTechnical Screen - RbcroyalbankEasyData EngineerExplain pandas and SQL basics
This question evaluates familiarity with pandas data structures (Series vs DataFrame), SQL filtering semantics (WHERE vs HAVING), and practical SQL aggregation for detecting duplicate records, assessing competencies in data manipulation, aggregation, and basic data quality checks.
Data Manipulation (SQL/Python)Technical Screen - RbcroyalbankMediumMachine Learning EngineerNavigate Conflict, Compromise, and a Revealed Weakness
This set of behavioral prompts evaluates a candidate's ability to navigate professional friction, demonstrate self-awareness regarding technical limitations, and exercise judgment in collaborative software development.
Behavioral & LeadershipTechnical ScreenBehavioralConflict ResolutionLeadership - RbcroyalbankMediumMachine Learning EngineerProcess Operations for an In-Memory Key-Value Store
This problem tests proficiency with hash map operations and handling data retrieval edge cases within an in-memory data store.
Coding & AlgorithmsTechnical ScreenHash MapData StructuresAlgorithms - Read.AiMediumSoftware EngineerCompare Sentences Using Transitive Synonym Groups
Compare two tokenized sentences using undirected, transitive synonym relationships. Sentence lengths and word positions must match, while a union-find or graph traversal groups chained synonyms and leaves unknown words equivalent only to themselves.
Coding & AlgorithmsTechnical Screen - RedditMediumMachine Learning EngineerAnalyze CTR Data and Train Model
This problem tests the end-to-end workflow of building a CTR prediction model, focusing on feature engineering for high-cardinality categorical data and handling temporal leakage.
Machine LearningOnsiteFeature EngineeringCTR PredictionData Leakage - RedditMediumMachine Learning EngineerBuild and evaluate click prediction models
This question evaluates skills in supervised probabilistic modeling for click-through rate prediction, including model selection, calibration, evaluation metric choice, cross-validation, hyperparameter tuning, and production concerns like serving and monitoring.
Machine LearningTechnical Screen - RedditEasyData ScientistCollaborate with PM and Eng as DS
This question evaluates a data scientist's cross-functional collaboration, prioritization, ownership, stakeholder management, and influencing skills when engaging with product managers and engineers.
Behavioral & LeadershipOnsite - RedditHardData ScientistCommunicate and de-risk a non-experimental launch
This question evaluates a data scientist's competency in interpreting causal-inference results from a Synthetic Control, communicating evidence strength and residual risks to executives and skeptical partners, structuring rollout governance and rollback criteria, and managing bias control, dissent and cross-functional accountability.
Behavioral & LeadershipTechnical Screen - RedditMediumSoftware EngineerDefend a Schema Decision Through a Real Technical Challenge
This question assesses a candidate's ability to articulate the trade-offs behind a core architectural decision, focusing on data modeling, scalability, and technical growth through post-launch reality.
Behavioral & LeadershipTechnical ScreenSystem DesignData ModelingDatabase Internals - RedditMediumMachine Learning EngineerDescribe a failure and a success
This question evaluates ownership, communication, accountability, and impact assessment within engineering and machine learning contexts, focusing on incident management, model launches, delivery outcomes, and collaboration.
Behavioral & LeadershipOnsite - RedditEasyData ScientistDesign A/B test for AI chat box
This question tests the ability to define metrics, design an A/B testing framework, and account for potential biases when deploying a new AI-powered chat feature.
Analytics & ExperimentationOnsiteA/B TestingProduct AnalyticsMetrics - RedditHardSoftware EngineerDesign Add, Overwrite, Undo, and Redo for Billing State
Design an in-memory billing account state manager that supports delta adjustments, absolute balance overwrites, and robust undo/redo capabilities.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignStackState Machine - RedditMediumMachine Learning EngineerDesign Comment Prediction Ranking System
This question assesses the ability to design an end-to-end ML pipeline for ranking comments by predicted user engagement probability, balancing accuracy, latency, and system scalability.
ML System DesignTechnical ScreenRankingRecommendation SystemsFeature Engineering - RedditMediumBackend EngineerDesign Tennis Scoring with Pluggable Rules
Design a testable tennis game scoring component with point recording, readable scores, terminal-state handling, and stable player identity. Keep standard Deuce and Advantage rules separate from pluggable alternatives such as no-ad scoring, and discuss audit history and serialization.
Software Engineering FundamentalsTechnical Screen - RedditMediumMachine Learning EngineerDesign a Comment-Likelihood Prediction System with a Focus on Feature and Model Serving
Design a machine-learning system that predicts how likely a user is to comment on a post, covering label definition, features and model choice, with most of the depth on feature serving and model serving. Tests online and offline feature consistency, feature freshness, latency budgets and safe model deployment.
ML System DesignOnsite - RedditEasyMachine Learning EngineerDesign a Tennis Scoring System
This problem tests your ability to model state transitions, encapsulate logic using object-oriented principles, and implement conditional rule-based scoring in a competitive game.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignState MachineLogic - RedditHardSoftware EngineerDesign a feature store with CI/CD and reliability
This question evaluates a candidate's competencies in designing scalable, low-latency feature stores and end-to-end ML infrastructure, covering feature ingestion, point-in-time correctness, serving APIs, CI/CD for feature pipelines, metadata/lineage, and reliability engineering.
ML System DesignTechnical Screen - RedditEasySoftware EngineerDesign a game leaderboard service
This question tests the ability to design a high-throughput, low-latency leaderboard service capable of handling frequent rank updates and read requests for millions of active users.
System DesignOnsiteCachingDatabasesScalability - RedditMediumMachine Learning EngineerDesign a video recommendation system
This question evaluates ML system design competencies for a Machine Learning Engineer, covering infrastructure architecture, logging and observability, feature pipelines, model training/evaluation, serving reliability, and privacy-aware data practices within the ML System Design domain.
ML System DesignOnsite - RedditHardMachine Learning EngineerDesign comment ranking
This problem evaluates your ability to design a scalable comment ranking system that balances engagement, relevance, and safety, while addressing the challenges of low-latency inference and data freshness.
ML System DesignOnsiteRankingInformation RetrievalRecommender Systems - RedditMediumSoftware EngineerDesign comment-likelihood prediction platform
This question evaluates a candidate's competency in ML system design and infrastructure, covering feature pipelines, feature store strategy, training/serving consistency, online inference at high QPS, and production monitoring.
ML System DesignOnsite - RedditEasyData ScientistDesign experiment for ads in chat with budgets
This question tests the ability to design an A/B test for a new ad placement in a chat feature while accounting for budget constraints and potential network effects.
Analytics & ExperimentationOnsiteA/B TestingCausal InferenceExperiment Design - RedditHardData ScientistEstimate Incremental Search-Ad Revenue Without an A/B Test
Design an evaluation framework to estimate the incremental search-ad revenue of a small-scale manual pilot, incorporating variance reduction, inference under heavy-tailed revenue, and ecosystem cannibalization.
Analytics & ExperimentationOnsiteexperimentationcausal-inferencead-tech - RedditMediumMachine Learning EngineerExclude Cat- or Dog-Related Comments and All Their Replies by Reader Mode
Given discussion comments that form reply trees, each flagged as being about cats, dogs, both or neither, return every comment a cat person or a dog person should not see, where hiding a comment also hides all of its replies. Tests reasoning over tree structure, edge cases such as missing parents, and complexity analysis.
Coding & AlgorithmsOnsite - RedditMediumSoftware EngineerExplain How You Use AI in Engineering Work
Prepare a concrete account of using AI in engineering, including what you delegated, what information you withheld, and how you verified or rejected its output. Discuss correctness, privacy, licensing, maintainability, and continued human accountability.
Behavioral & LeadershipTechnical Screen
Ramp
Reddit