practice a little every day, paws crossed ♡
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
- Companies
- 11
- Topics
- 8
- Languages
Showing 7801–7830 of 9403 results
- SnapchatHardSoftware EngineerDesign a streaming ads system
This question evaluates a candidate's competency in designing low-latency, scalable, and privacy-compliant ad-insertion platforms, encompassing distributed systems, real-time bidding integration, ad format and measurement standards, content and identity signal processing, pacing/frequency control, and multi-region fault tolerance.
System DesignOnsite - SnapchatMediumSoftware EngineerDesign a swipeable video feed backend
This question evaluates system-design competence for building scalable, low-latency backend services that handle feed vending, pagination, personalized ranking, caching, storage, and content eligibility; it is categorized under System Design and tests practical application of distributed systems and backend data engineering principles.
System DesignOnsite - SnapchatHardMachine Learning EngineerDesign an Advertising or Recommendation Model End to End
Design a recommendation model from labels and sampling through features, loss, ranking metrics, online experiments, and production debugging.
ML System DesignOnsite - SnapchatMediumMachine Learning EngineerDesign an ads ranking ML system
This question tests the ability to design an end-to-end ads ranking pipeline, focusing on latency constraints, mitigation of position bias, and balancing multi-objective optimization.
ML System DesignOnsiteAds RankingMulti-Task LearningSystem Design - SnapchatHardData ScientistDesign an experiment for spam filtering impact
This question evaluates experimental design and causal inference skills for measuring the effect of a stricter spam filter on same-day friend-request acceptance, covering hypothesis specification, interference and randomization choices, metric definition and windows, power calculation, and handling incomplete labels.
Analytics & ExperimentationTechnical Screen - SnapchatMediumSoftware EngineerDesign and Implement a Thread-Safe LRU Cache
Design an `O(1)` LRU cache and extend it with clear thread-safety semantics. The solution covers hash-map and doubly linked-list invariants, boundary cases, linearization points, single-lock correctness, sharding trade-offs, eviction callbacks, and concurrent testing.
Software Engineering FundamentalsTechnical Screen - SnapchatHardSoftware EngineerDesign product co-occurrence analytics API
This question evaluates design skills for RESTful APIs and backend systems, covering API schema and versioning, authentication and rate limiting, caching and cache invalidation, storage models, precomputation pipelines, consistency and staleness bounds, monitoring, and operational concerns.
System DesignOnsite - SnapchatMediumSoftware EngineerDesign real-time ad impression and spend tracking
This question tests the ability to design a high-throughput, fault-tolerant system capable of tracking massive volumes of ad impression events and calculating real-time budget spending.
System DesignOnsiteDistributed SystemsCachingHigh Throughput - SnapchatHardMachine Learning EngineerDesign real-time top-K POI retrieval on maps
This question evaluates a candidate's ability to design scalable, low-latency real-time retrieval and personalized ranking systems for geospatial data, testing competencies in spatial indexing and tiling, candidate generation versus re-ranking pipelines, caching and freshness strategies, latency and availability SLOs, deduplication and pagination, and model/ANN decision-making; the domain is ML system design for map-based POI retrieval and it assesses both high-level architectural reasoning and practical engineering trade-offs. Such problems are commonly asked to assess how an engineer balances consistency, stability and freshness under strict latency and availability targets while reasoning about operational concerns like load shedding, offline versus online evaluation, instrumentation and cost.
ML System DesignOnsite - SnapchatHardSoftware EngineerDesign ride-hailing and price alert systems
This question evaluates a candidate's competency in large-scale system design, including distributed architecture, real-time data processing, geo-spatial indexing and low-latency matching for ride-hailing, plus scalable ingestion, time-series storage, rule evaluation, and reliable notification pipelines for price-tracking, within the System Design domain. It is commonly asked to assess architectural trade-offs around scalability, consistency, availability, fault tolerance and operational concerns, testing practical application of architectural patterns alongside conceptual understanding of data modeling and real-time constraints.
System DesignOnsite - SnapchatHardSoftware EngineerDesign short URL service with click counting
This question tests the ability to design a distributed URL shortening service, focusing on system architecture, database partitioning, and strategies for high-frequency write operations like click tracking.
System DesignTechnical ScreenScalabilityDatabaseCaching - SnapchatMediumMachine Learning EngineerDesign short-video retrieval with sparse text
This question evaluates expertise in ML system design, large-scale information retrieval and recommendation engineering, with emphasis on multi-modal video representation, sparse-text handling, indexing/ANN choices, low-latency online serving, and bias mitigation such as popularity bias.
ML System DesignTechnical Screen - SnapchatMediumMachine Learning EngineerDetermine Whether Courses Can Be Completed
This question evaluates understanding of graph modeling, directed cycle detection, and dependency resolution in the context of course prerequisites. It is commonly asked in the coding & algorithms domain because it reveals a candidate's algorithmic problem-solving ability and practical application of graph algorithms (expected O(V+E) time), testing practical implementation skills rather than only conceptual theory.
Coding & AlgorithmsTechnical Screen - SnapchatMediumMachine Learning EngineerDetermine and print expression to reach target
This question evaluates skill in constructing and evaluating arithmetic expressions, handling numerical stability (including division-by-zero and floating-point tolerance), and reasoning about combinatorial permutations of numbers and operators.
Coding & AlgorithmsOnsite - SnapchatMediumSoftware EngineerDetermine blockage and parse HTML tokens
This question evaluates computational geometry and graph connectivity for the circular-stones subproblem and parsing plus tree data-structure manipulation for the HTML-like token subproblem, measuring skills in modeling spatial overlap, graph traversal/union operations, hierarchical DOM construction, and dynamic node insertion/deletion.
Coding & AlgorithmsOnsite - SnapchatMediumData ScientistDiagnose Violations of Linear Regression Assumptions
Tests a candidate's systematic understanding of ordinary least squares (OLS) regression assumptions, distinguishing how specific violations compromise unbiased point estimation, standard error inference, and predictive accuracy.
Statistics & MathTechnical ScreenStatisticsLinear RegressionEconometrics - SnapchatMediumSoftware EngineerEngineering Manager Behavioral: Motivation, Taking Feedback and Leading a Reorganization
A Snapchat engineering manager behavioral question in three parts: what motivates you in this role, how you take critical feedback, and how you led a team through a restructuring. It tests self-awareness, growth from feedback, transparent communication and care for people during change.
Behavioral & LeadershipOnsite - SnapchatMediumSoftware EngineerEngineering Manager Scenarios: Promotion Cases, Team Conflict and Performance Plans
A Snapchat engineering manager leadership question covering three people-management scenarios: building a case to promote an engineer, resolving conflict on your team, and managing underperformance through a performance improvement plan. It tests fairness, judgment and coaching.
Behavioral & LeadershipOnsite - SnapchatMediumMachine Learning EngineerEvaluate an Integer Arithmetic Expression String With Operator Precedence
A coding problem to evaluate a string expression of non-negative integers with addition, subtraction, multiplication and integer division, respecting operator precedence and left-to-right order, with no parentheses and no eval. It tests parsing, precedence handling and truncating integer division.
Coding & AlgorithmsTechnical Screen - SnapchatMediumMachine Learning EngineerExplain BatchNorm, optimizers, and L1/L2
This question tests your conceptual understanding of how normalization, optimization strategies, and regularization techniques influence training stability, convergence, and model sparsity.
Machine LearningOnsiteDeep LearningOptimizationRegularization - SnapchatMediumMachine Learning EngineerExplain CLIP, contrastive losses, and retrieval limits
This question evaluates understanding of multi-modal representation learning and retrieval systems, covering CLIP-style joint image–text encoders, contrastive loss families, embedding-based retrieval limitations, alternative retrieval paradigms, and issues like popularity bias.
Machine LearningTechnical Screen - SnapchatMediumMachine Learning EngineerExplain Core ML Concepts
This question tests the ability to explain foundational machine learning trade-offs and architectural nuances in deep neural networks.
Machine LearningOnsiteDeep LearningOptimizationStatistics - SnapchatHardMachine Learning EngineerExplain Core ML Training, Evaluation, and Transformer Concepts
Tests end-to-end understanding of machine learning fundamentals, covering regularization against overfitting, handling severe class imbalance, optimizer mechanics, threshold calibration, and Transformer self-attention dynamics.
Machine LearningTechnical Screenmachine-learningdeep-learningtransformers - SnapchatHardMachine Learning EngineerExplain Embeddings, Positive Labels, and Retraining in a Recommendation Project
Discuss recommendation-project ownership, embeddings, positive labels and retraining with attention to exposure bias and data leakage.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain LLM tuning and transformer basics
This question evaluates competencies in large language model tuning and Transformer fundamentals, covering fine-tuning strategies, dataset construction and labeling, model adaptation choices, loss functions and evaluation metrics, regularization techniques, optimizer selection, self-attention and multi-head attention, and the end-to-end Transformer decoder mathematical steps. It is commonly asked in Machine Learning interviews because it probes both conceptual understanding and practical application—examining architectural trade-offs, optimization and regularization decisions, deployment constraints, and reasoning about failure modes such as overfitting and hallucination within the Machine Learning domain.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain Neural-Network Regularization and Dropout
Explain L1, L2, dropout scaling, train/inference behavior, early stopping, and the difference between gradient penalties and decoupled weight decay.
Machine LearningOnsite - SnapchatMediumMachine Learning EngineerExplain Overfitting and Transformer Attention
This question evaluates understanding of model generalization and regularization techniques alongside Transformer self-attention and positional encoding, assessing competencies in diagnosing overfitting, applying appropriate mitigation strategies, and interpreting attention mechanisms.
Machine LearningTechnical Screen - SnapchatHardMachine Learning EngineerExplain Vanishing Gradients in Neural-Network Training
Diagnose vanishing gradients through the chain rule, activation and gradient checks, and architecture-appropriate training remedies.
Machine LearningTechnical Screen - SnapchatMediumSoftware EngineerExplain median vs mean for L1/L2
This question evaluates understanding of central tendency and loss functions—specifically the roles of median and mean under L1 (absolute) and L2 (squared) norms—and the geometric extension of these concepts to two-dimensional data.
Statistics & MathTechnical Screen - SnapchatMediumSoftware EngineerFind Duplicate Files with a Hand-Written Directory Traversal
This problem tests iterative directory traversal techniques, efficient file hashing strategies to handle large data, and the ability to manage edge cases like file system inconsistencies or symbolic links.
Software Engineering FundamentalsTechnical ScreenFile SystemHashingIteration
Snapchat