ML System Design interview questions

Showing 241–270 of 332 results

• Design enterprise RAG 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 enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  OpenAIMachine Learning EngineerML System DesignTechnical Screen
• Select high-quality math documents from crawlsHard

  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.

  OpenAIMachine Learning EngineerML System DesignOnsite
• Design an E-Commerce Recommendation System from Browsing HistoryMedium

  This problem tests the ability to architect a scalable, low-latency recommendation engine that translates real-time user activity into personalized product suggestions.

  OracleSoftware EngineerML System DesignOnsite
• Design scalable, highly available GenAI servingHard

  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.

  OracleSoftware EngineerML System DesignOnsite
• Evaluate Subjective and Nondeterministic Agent OutputsHard

  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.

  OracleSoftware EngineerML System DesignTechnical Screen
• Design an ML inference orchestration platformHard

  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.

  Palo Alto NetworksSoftware EngineerML System DesignOnsite
• Design a traditional fraud detection systemHard

  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.

  PayPalMachine Learning EngineerML System DesignOnsite
• Design personalized discovery recommendationsMedium

  This problem evaluates your ability to design a scalable, real-time recommendation system that balances personalization with content discovery in an information-seeking environment.

  PerplexitySoftware EngineerML System DesignOnsite
• Design trending queries ranking systemMedium

  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.

  PerplexitySoftware EngineerML System DesignOnsite
• Implement resilient LLM provider poolHard

  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.

  PerplexitySoftware EngineerML System DesignTechnical Screen
• Design Detection Systems for Risk and SafetyMedium

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design Pin recommendation systemHard

  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.

  PinterestSoftware EngineerML System DesignOnsite
• Design User and Item Embeddings from Long HistoriesMedium

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design a real-time home feed rankerHard

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design an ads system to improve CTRHard

  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.

  PinterestMachine Learning EngineerML System DesignTechnical Screen
• Design an unsafe content detection systemHard

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design notification and feed recommendersMedium

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Rank Newly Launched Ads Under Cold StartMedium

  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.

  PinterestMachine Learning EngineerML System DesignOnsite
• Design RAG Evaluation and DebuggingMedium

  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.

  PlaidMachine Learning EngineerML System DesignOnsite
• Design a Fraud Detection SystemMedium

  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.

  PlaidMachine Learning EngineerML System DesignTechnical Screen
• Design Comment Prediction Ranking SystemMedium

  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.

  RedditMachine Learning EngineerML System DesignTechnical Screen
• Design a Comment-Likelihood Prediction System with a Focus on Feature and Model ServingMedium

  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.

  RedditMachine Learning EngineerML System DesignOnsite
• Design a feature store with CI/CD and reliabilityHard

  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.

  RedditSoftware EngineerML System DesignTechnical Screen
• Design a video recommendation systemMedium

  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.

  RedditMachine Learning EngineerML System DesignOnsite
• Design comment rankingHard

  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.

  RedditMachine Learning EngineerML System DesignOnsite
• Design comment-likelihood prediction platformMedium

  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.

  RedditSoftware EngineerML System DesignOnsite
• Design a Game Recommendation SystemMedium

  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.

  RobloxMachine Learning EngineerML System DesignOnsite
• Design a Static Audio Detection SystemHard

  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.

  RobloxSoftware EngineerML System DesignOnsite
• Design a game genre classifierMedium

  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.

  RobloxMachine Learning EngineerML System DesignOnsite
• Design a game recommendation modeling approachEasy

  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.

  RobloxSoftware EngineerML System DesignTechnical Screen