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ML System Design 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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- 546
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- 11
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- 8
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Showing 151–180 of 186 results
- 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 - RobloxMediumMachine Learning EngineerDesign a Game Recommendation System
This problem tests your ability to design a multi-stage recommendation architecture tailored to a gaming platform, focusing on user-specific constraints like age-appropriateness and the challenges of the long-tail game distribution.
ML System DesignOnsiteRecommendation SystemsSystem DesignRanking - RobloxHardSoftware EngineerDesign a Static Audio Detection System
This question evaluates system design and ML integration skills for building a scalable, reliable offline audio detection pipeline, including ingestion, preprocessing, STT and spectral feature extraction, rule-based post-processing, artifact persistence, and human-in-the-loop review within the ML system design domain.
ML System DesignOnsite - RobloxMediumMachine Learning EngineerDesign a game genre classifier
This question assesses the ability to design an end-to-end multi-modal machine learning system for classifying user-generated game content based on diverse input features and real-time serving requirements.
ML System DesignOnsiteMulti-modalNLPComputer Vision - RobloxHardSoftware EngineerOutline the ML inference and labeling pipeline
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.
ML System DesignOnsite - RogoMediumMachine Learning EngineerManage Context Windows in an Applied AI System
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.
ML System DesignOnsiteLLMRAGSystem Design - Scale AIEasySoftware EngineerDesign an LLM API pipeline
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.
ML System DesignOnsite - Scale AIMediumSoftware EngineerDesign pipeline using classification and embedding services
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.
ML System DesignOnsite - ShopifyMediumMachine Learning EngineerDesign Personalized Product Feeds
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.
ML System DesignOnsite - ShopifyHardMachine Learning EngineerDesign a baseline loan recommendation system
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.
ML System DesignOnsite - ShopifyHardMachine Learning EngineerDesign a hierarchical multi-label classifier
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.
ML System DesignOnsite - ShopifyMediumMachine Learning EngineerDesign search autocomplete ML system
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.
ML System DesignOnsite - SnapchatMediumMachine Learning EngineerDesign a Product Tagging Pipeline
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.
ML System DesignOnsiteComputer VisionNLPMulti-modal - SnapchatMediumMachine Learning EngineerDesign a Trustworthy Ranking System
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.
ML System DesignOnsiteRankingTrust & SafetyMulti-objective Optimization - SnapchatHardMachine Learning EngineerDesign a harmful content detection system
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.
ML 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 - 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 - SnowflakeMediumMachine Learning EngineerDesign a Pipeline That Distills Reusable Agent Skills for Enterprise Customers
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.
ML System DesignOnsite - SpotifyMediumMachine Learning EngineerDesign Podcast Recap Generation
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.
ML System DesignOnsite - SpotifyMediumMachine Learning EngineerDesign a Sequential Personalized Playlist Recommender
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.
ML System DesignOnsiteRecSysML-ArchitectureLatency - SpotifyHardMachine Learning EngineerPredict Next-Week Streaming Engagement at Event Scale
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.
ML System DesignOnsiteSystem DesignMLOpsRecommendation Systems - Startups.ComMediumMachine Learning EngineerDesign efficient Transformer inference with KV cache
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.
ML System DesignOnsite - TeslaHardMachine Learning EngineerDesign an LLM math-solving chain
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.
ML System DesignOnsite - TikTokMediumMachine Learning EngineerDesign a model to choose dynamic K
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.
ML System DesignOnsite - UberMediumMachine Learning EngineerDesign a restaurant recommendation system
An Uber MLE onsite ML system design question: design a personalized restaurant recommendation system for a food delivery marketplace's home feed. It tests candidate generation and ranking, feature engineering and training data, cold-start and exploration, low-latency serving, and offline/online evaluation, plus marketplace concerns like availability, ETA, diversity, fairness, and business rules.
ML System DesignOnsite - UberHardMachine Learning EngineerDesign real-time grid ETA for drivers
This question evaluates a candidate's competency in real-time ML system design, covering streaming data ingestion, GPS noise handling and denoising, online feature computation, low-latency model serving, and operational monitoring.
ML System DesignOnsite - UnityMediumSoftware EngineerDesign a Feature Store for Offline Training and Low-Latency Online Serving
Design a feature store that ingests high-volume event data, produces point-in-time correct training sets, and serves the latest features at low latency in production. It tests streaming and batch feature computation, leakage-free historical joins, online store design, and keeping training and serving consistent.
ML System DesignOnsite - VantaMediumSoftware EngineerReview and Debug an AI Chatbot
Design an end-to-end debugging, observability, and evaluation architecture for a multi-tenant enterprise AI compliance chatbot experiencing hallucinations, latency spikes, and policy violations.
ML System DesignOnsiteLLM ObservabilityRAGData Privacy - Walmart LabsEasyMachine Learning EngineerDesign a Conversational Agent for Finding Vacation Rentals
Design a multi-turn assistant that turns a traveler's free-form trip description into structured, grounded rental search results. Tests where to use an LLM versus deterministic search, clarification strategy, safety against hallucination and injection, and evaluation against classic search.
ML System DesignOnsitellm-agentssearchretrieval
Reddit
Roblox
Scale AI
Shopify
Snapchat
Snowflake
Spotify
Tesla
TikTok
Uber
Vanta
Walmart Labs