practice a little every day, paws crossed ♡
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.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 241–270 of 332 results
- OpenAIHardMachine Learning EngineerDesign enterprise RAG 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 enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerSelect high-quality math documents from crawls
This question assesses the ability to design a scalable pipeline that ingests, cleans, extracts, and filters high-quality mathematical content from diverse, noisy web-crawled sources.
ML System DesignOnsiteSystem DesignNLPData Engineering - OracleMediumSoftware EngineerDesign an E-Commerce Recommendation System from Browsing History
This problem tests the ability to architect a scalable, low-latency recommendation engine that translates real-time user activity into personalized product suggestions.
ML System DesignOnsiteSystem DesignRecommendation SystemsScalability - OracleHardSoftware EngineerDesign scalable, highly available GenAI serving
This question evaluates understanding of scalable, highly available generative AI inference platforms and associated competencies in distributed systems, ML model serving, autoscaling and GPU scheduling, global request routing, model/version management, stateful dependency handling, observability, and rate limiting.
ML System DesignOnsite - OracleHardSoftware EngineerEvaluate Subjective and Nondeterministic Agent Outputs
Create an evaluation program for subjective, nondeterministic AI-agent outputs when direct user feedback is sparse. Cover stratified tasks, versioned rubrics, repeated sampling, calibrated human review, judge agreement and bias, uncertainty-aware comparisons, severe-failure gates, online signals, and rollback criteria.
ML System DesignTechnical Screen - Palo Alto NetworksHardSoftware EngineerDesign an ML inference orchestration platform
This question evaluates system-design and ML infrastructure competencies, focusing on orchestration of multi-model inference workflows, distributed data flow and storage, versioning and model management, scaling and deployment, failure handling, observability, and multi-tenant security and quota management.
ML System DesignOnsite - PayPalHardMachine Learning EngineerDesign a traditional fraud detection system
This question evaluates a Machine Learning Engineer's competency in end-to-end ML system design for real-time payments fraud detection, including labeling under delayed confirmations, handling extreme class imbalance and sampling, feature engineering across behavioral, graph, device and merchant signals, model selection for latency and scale, and production scoring and monitoring architecture. It is commonly asked in the ML System Design category to assess how an engineer balances low-latency decision-making with delayed sparse labels, calibration and threshold trade-offs, operational scalability and resiliency, and drift/adversarial detection, testing both conceptual understanding and practical application.
ML System DesignOnsite - PerplexityMediumSoftware EngineerDesign personalized discovery recommendations
This problem evaluates your ability to design a scalable, real-time recommendation system that balances personalization with content discovery in an information-seeking environment.
ML System DesignOnsiteRecommender SystemsML InfrastructureRanking - PerplexityMediumSoftware EngineerDesign trending queries ranking system
This question tests the ability to design a high-throughput, real-time pipeline that identifies and ranks trending search queries, requiring trade-offs between freshness, accuracy, and system latency.
ML System DesignOnsiteSystem DesignReal-time StreamingRanking - PerplexityHardSoftware EngineerImplement resilient LLM provider pool
This English-language summary evaluates a candidate's ability to design a resilient, multi-provider LLM client library encompassing provider management, governance (rate limiting and quotas), resilience mechanisms (health checks, timeouts, circuit breakers), routing (cost- and latency-aware load balancing, retries and fallbacks), observability, security, and concurrency. Commonly asked in the ML System Design domain to assess architectural decision-making and trade-off reasoning for availability, cost, latency, and operational robustness, the question targets practical application and architectural-level thinking rather than purely conceptual or low-level implementation details.
ML System DesignTechnical Screen - PinterestMediumMachine Learning EngineerDesign Detection Systems for Risk and Safety
This question tests the ability to design an end-to-end classification system for detecting harmful content (e.g., spam or toxic images) while balancing precision and recall at scale.
ML System DesignOnsiteComputer VisionNLPContent Moderation - PinterestHardSoftware EngineerDesign Pin recommendation system
This question evaluates ability to design a large-scale personalized home feed recommendation system, testing skills in ML system design, recommender algorithms, retrieval and ranking architectures, feature engineering, data pipelines, low-latency serving, evaluation metrics, safety, and privacy.
ML System DesignOnsite - 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 - 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 - 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 - 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 - RobloxEasySoftware EngineerDesign a game recommendation modeling approach
This problem tests the ability to design an end-to-end recommendation engine for a massive-scale gaming platform, focusing on user-item interaction modeling and handling cold-start scenarios.
ML System DesignTechnical ScreenRecommender SystemsML DesignRetrieval
OpenAI
Oracle
Palo Alto Networks
PayPal
Perplexity
Pinterest
Plaid
Reddit
Roblox