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Machine Learning Engineer 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
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Showing 121–150 of 178 results
- PinterestMediumMachine Learning EngineerDesign User and Item Embeddings from Long Histories
Design an end-to-end candidate retrieval system using user and visual-item embeddings capable of processing ultra-long user histories (up to 160,000 interactions) while handling real-time freshness, cold-start items, and privacy constraints.
ML System DesignOnsiteRecommendation SystemsVector EmbeddingsCandidate Retrieval - PinterestHardMachine Learning EngineerDesign a real-time home feed ranker
This problem evaluates your ability to design a multi-stage recommendation architecture capable of integrating real-time user signals, managing feature latency, and balancing ranking accuracy with system throughput.
ML System DesignOnsiteSystem DesignRecommendation SystemsReal-time - PinterestHardMachine Learning EngineerDesign an ads system to improve CTR
This question evaluates machine learning system design competency for improving ad click-through rate, testing understanding of metrics, end-to-end architecture, training data and embeddings, and operational issues like cold start, bias and delayed feedback within advertising and recommender domains.
ML System DesignTechnical Screen - PinterestHardMachine Learning EngineerDesign an unsafe content detection system
This problem evaluates your ability to design a scalable, multimodal machine learning pipeline for detecting policy-violating content on a large-scale visual discovery platform.
ML System DesignOnsiteMultimodal LearningReal-time InferenceContent Moderation - PinterestMediumMachine Learning EngineerDesign notification and feed recommenders
This question tests the ability to design a dual-purpose recommendation architecture capable of handling real-time push notifications and personalized feed updates at scale.
ML System DesignOnsiteRecommender SystemsRankingScalability - PinterestMediumMachine Learning EngineerRank Newly Launched Ads Under Cold Start
Design a machine learning system to rank and explore cold-start ads launched within the past seven days, balancing marketplace revenue, user engagement, and advertiser budget delivery.
ML System DesignOnsiteml system designcold startad tech - PlaidMediumMachine Learning EngineerDesign RAG Evaluation and Debugging
Design a comprehensive evaluation, monitoring, and debugging framework for a production Retrieval-Augmented Generation (RAG) system operating in a high-stakes financial data domain. The candidate must address multi-stage metrics, CI/CD regression testing, and root-cause localization for retrieval vs. generation failures.
ML System DesignOnsiteRAGLLM EvaluationModel Monitoring - PlaidMediumMachine Learning EngineerDesign a Fraud Detection System
This question evaluates competency in ML system design for fraud detection within fintech data-connectivity platforms, testing real-time scoring, feature engineering (including relational/graph signals), label strategy, model training and serving, and operational concerns such as latency, privacy, monitoring, and robustness.
ML System DesignTechnical Screen - 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 - 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 - 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 - 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 - 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 - RobloxHardMachine Learning EngineerDesign a real-time game recommendation system
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.
ML System DesignTechnical Screen - 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 - RunwayHardMachine Learning EngineerDesign a scalable video search system
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.
ML System DesignTechnical Screen - 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 a ranking system pipeline
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.
ML System DesignTechnical Screen - SnapchatMediumMachine Learning EngineerDesign a short-video recommender for short-term interest
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.
ML System DesignTechnical ScreenRecommender SystemsReal-time InferenceSession-based Models - 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 - 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
Pinterest
Plaid
Reddit
Roblox
Shopify
Snapchat