ML System Design interview questions

Showing 271–300 of 332 results

• Design a real-time game recommendation systemHard

  Design a real-time game recommendation system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  RobloxMachine Learning EngineerML System DesignTechnical Screen
• Outline the ML inference and labeling pipelineHard

  Outline the ML inference and labeling pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  RobloxSoftware EngineerML System DesignOnsite
• Manage Context Windows in an Applied AI SystemMedium

  This question tests the ability to design a robust context-management layer for an LLM-based application that balances limited token capacity against competing sources of information.

  RogoMachine Learning EngineerML System DesignOnsite
• Design a scalable video search systemHard

  This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design a scalable video search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  RunwayMachine Learning EngineerML System DesignTechnical Screen
• Design a recommendation systemMedium

  This problem tests the ability to design an end-to-end recommendation engine, focusing on the pipeline from data ingestion to real-time inference and model evaluation.

  SalesforceSoftware EngineerML System DesignTechnical Screen
• Design an end-to-end recommendation systemHard

  Design an end-to-end recommendation system evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  SalesforceSoftware EngineerML System DesignTechnical Screen
• Design an LLM API pipelineEasy

  This question evaluates competency in integrating hosted LLMs into user-facing applications, including API request design, prompt engineering for structured output, output validation, latency and failure handling, and production readiness.

  Scale AISoftware EngineerML System DesignOnsite
• Design pipeline using classification and embedding servicesMedium

  This question evaluates a candidate's proficiency in ML system design, covering API design, service orchestration, data storage modeling, scalability strategies, and reliability/observability when integrating black-box classification and embedding services.

  Scale AISoftware EngineerML System DesignOnsite
• Design Personalized Product FeedsMedium

  This question evaluates a candidate's ability to design end-to-end machine learning systems for personalized product recommendation, assessing competencies in data collection, candidate generation, feature pipelines, model training, online serving, and re-ranking.

  ShopifyMachine Learning EngineerML System DesignOnsite
• Design a baseline loan recommendation systemHard

  This question evaluates understanding of ML-driven recommendation and credit-risk systems, including skills in personalization, feature design, risk calibration, fairness and regulatory compliance, explainability, and operational monitoring.

  ShopifyMachine Learning EngineerML System DesignOnsite
• Design a hierarchical multi-label classifierHard

  This question evaluates a candidate's competency in designing production-grade hierarchical multi-label classification systems, covering taxonomy normalization, label dependency modeling, loss and decoding strategies, class imbalance handling, evaluation metrics, data labeling at scale, and operational constraints such as latency, model size, and monitoring. Commonly asked in the ML System Design domain to assess both conceptual understanding of hierarchical label structures and practical application-level trade-offs in architecture, training, deployment, and maintenance.

  ShopifyMachine Learning EngineerML System DesignOnsite
• Design search autocomplete ML systemMedium

  This question evaluates an engineer's ability to design an ML-powered search autocomplete system, testing competencies in candidate generation versus ranking architectures, data sourcing and labeling, feature and model selection (including personalization and context), offline training and online serving, experimentation and monitoring, and safety and abuse mitigation within information retrieval. Commonly asked to assess how applicants balance production concerns such as latency, relevance, scalability, cold-start handling, and feedback-loop mitigation, it sits in the ML system design domain and emphasizes practical, applied engineering trade-offs rather than purely conceptual theory.

  ShopifyMachine Learning EngineerML System DesignOnsite
• Design a Product Tagging PipelineMedium

  This problem tests your ability to design a scalable multi-modal machine learning pipeline that processes diverse content types to generate standardized taxonomy tags for an e-commerce platform.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design a Trustworthy Ranking SystemMedium

  This problem tests the ability to design an end-to-end ML system for ranking content while balancing engagement metrics with safety, misinformation, and user trust.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design a harmful content detection systemHard

  This question evaluates proficiency in ML system design for harmful-content detection, assessing competencies in multimodal modeling, taxonomy and labeling strategies, data strategy, privacy-aware architecture, real-time inference, human-in-the-loop decisioning, and operational reliability for global consumer platforms.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design a ranking system pipelineHard

  This question evaluates competency in end-to-end ML system design for ranking and recommendation, covering problem definition, data and feature pipelines, offline training, online inference, model refresh, monitoring, and handling cold-start, exploration, feedback loops, and model drift.

  SnapchatMachine Learning EngineerML System DesignTechnical Screen
• Design a short-video recommender for short-term interestMedium

  This problem tests your ability to design a real-time recommendation system that captures dynamic user intent over short sessions, balancing latency, feature freshness, and algorithmic relevance.

  SnapchatMachine Learning EngineerML System DesignTechnical Screen
• Design an Advertising or Recommendation Model End to EndHard

  Design a recommendation model from labels and sampling through features, loss, ranking metrics, online experiments, and production debugging.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design an ads ranking ML systemMedium

  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.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design real-time top-K POI retrieval on mapsHard

  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.

  SnapchatMachine Learning EngineerML System DesignOnsite
• Design short-video retrieval with sparse textMedium

  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.

  SnapchatMachine Learning EngineerML System DesignTechnical Screen
• Design a Pipeline That Distills Reusable Agent Skills for Enterprise CustomersMedium

  Design a pipeline that automatically extracts reusable agent skills for enterprise customers from traces and documents, validates them and delivers them to agents. It tests LLM system design, outcome labeling, offline evaluation against a baseline, tenant isolation, versioning and drift monitoring.

  SnowflakeMachine Learning EngineerML System DesignOnsite
• Design Podcast Recap GenerationMedium

  This question evaluates system-design and machine-learning engineering competencies, including streaming versus batch ingestion, audio transcription and chunking, long-context retrieval and prompt/fine-tuning choices, model serving and cost-latency trade-offs, storage and indexing of transcripts and embeddings, evaluation of factual accuracy, and operational monitoring and recovery. Commonly asked to assess the ability to balance latency, throughput, cost, and accuracy in production ML pipelines, it is categorized as ML System Design and tests both conceptual understanding of trade-offs and practical application of scalable, reliable architecture.

  SpotifyMachine Learning EngineerML System DesignOnsite
• Design a Sequential Personalized Playlist RecommenderMedium

  This problem tests your ability to design a production-grade recommendation pipeline that balances immediate user intent (sequential context) with long-term personalization, while managing latency constraints at scale.

  SpotifyMachine Learning EngineerML System DesignOnsite
• Predict Next-Week Streaming Engagement at Event ScaleHard

  This question tests the ability to design an end-to-end ML system for user retention prediction, focusing on feature engineering, model scalability for massive user bases, and real-time inference latency.

  SpotifyMachine Learning EngineerML System DesignOnsite
• Design efficient Transformer inference with KV cacheMedium

  This question evaluates an engineer's competency in ML systems engineering, specifically understanding of decoder-only Transformer inference, KV cache semantics and which tensors are cached per layer, attention mechanics, memory layout, and correctness under branching and long-context workloads.

  Startups.ComMachine Learning EngineerML System DesignOnsite
• Design payment fraud detectionMedium

  This question evaluates a candidate's ability to design end-to-end machine learning systems for payment fraud detection, covering competencies in labeling, imbalanced learning, feature engineering across users/merchants/cards/devices/IPs and graph relationships, model selection, online serving, latency and feature freshness, monitoring, experimentation, and adversarial robustness. As an ML System Design problem for a Machine Learning Engineer role, it is commonly asked to probe trade-offs between fraud loss, false positives, user friction and latency and requires both conceptual understanding and practical application of production ML concerns such as evaluation metrics, thresholding, retraining, and operational constraints.

  StripeMachine Learning EngineerML System DesignTechnical Screen
• Design an LLM math-solving chainHard

  Design an LLM math-solving chain evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TeslaMachine Learning EngineerML System DesignOnsite
• Model other agents in simulationHard

  This question evaluates a candidate's competency in modeling other agents within simulation environments, focusing on designing agent behavior models and assessing their impact on training and evaluation robustness.

  TeslaMachine Learning EngineerML System DesignTechnical Screen
• Design a model to choose dynamic KMedium

  This question evaluates a candidate's competency in ML system design and decision-focused modeling for recommender pipelines, focusing on retrieval-ranking trade-offs, latency and compute cost constraints, and dynamic per-request selection of K.

  TikTokMachine Learning EngineerML System DesignOnsite