ML System Design Interview Questions

Showing 121–150 of 186 results

• NuroHard·Software Engineer
  Design a Shared Distributed ML Training Platform

  Design a multi-tenant distributed machine learning training platform that orchestrates GPU clusters, enforces team quotas, guarantees gang scheduling, and handles node-level checkpointing and recovery.

  ML System DesignOnsiteml-system-designdistributed-systemscluster-management
• OpenAIHard·Machine Learning Engineer
  Design LLM search handling long token inputs

  Design an LLM search and RAG system that handles long documents and long user queries. Covers chunking, hierarchical retrieval, query compression, context budgeting, citations, latency, and evaluation.

  ML System DesignOnsite
• OpenAIMedium·Machine Learning Engineer
  Design a Real-Time Sensor Intelligence System

  This question evaluates proficiency in end-to-end machine learning system design for real-time sensor-driven products, covering sensor choice trade-offs, data collection and labeling pipelines, model selection and real-time inference, and operational constraints such as latency, reliability, privacy, efficiency, and power.

  ML System DesignOnsite
• OpenAIHard·Software Engineer
  Design a Text-to-Video Generation Platform (Sora-style)

  This problem evaluates your ability to design a distributed ML infrastructure that handles high-latency, resource-intensive inference jobs while ensuring scalability, reliability, and content safety.

  ML System DesignOnsiteDistributed SystemsML InfrastructureTask Queues
• OpenAIHard·Software Engineer
  Design a Text-to-Video Generation System

  This question evaluates a candidate's competency in designing scalable ML inference platforms, including GPU resource management and scheduling, asynchronous job orchestration and state-machine-driven job lifecycles, failure classification and recovery, artifact storage, rate limiting, and monitoring for quality and latency.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a chatbot fallback for unknown questions

  This question evaluates a candidate's competency in ML system design, focusing on handling model uncertainty, retrieval-augmented components, tool integration, decision policies, and operational evaluation within the ML System Design domain.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a harmful video content moderation system

  This question evaluates a candidate's competency in designing scalable, multi-modal machine learning systems for content moderation, covering architecture, model strategy, labeling and human-in-the-loop workflows, metrics, auditability, and defenses against adversarial evasion.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a recommendation system end-to-end

  This question evaluates a candidate's competency in large-scale machine learning system design, including architecture for candidate generation and ranking, feature engineering across real-time and batch pipelines, training and serving workflows, and monitoring for fairness and safety.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design an AWS fine-tuning platform for LLMs

  This question evaluates a candidate's competency in designing cloud-native ML platforms for fine-tuning large language models, covering distributed training, multi-tenant security and isolation, cost controls, reproducibility, observability, checkpointing, and deployment workflows.

  ML System DesignOnsite
• OpenAIMedium·Machine Learning Engineer
  Design an Agent Harness and Evaluation System

  Design a safe harness and evaluation platform for a tool-using AI agent. Address isolated execution, repeatable task sets, trajectory tracing, deterministic and rubric-based graders, repeated trials, privacy, regression gates, and reward-hacking risks.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design an image/video near-duplicate detection system

  This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design and optimize a RAG system

  An OpenAI machine learning engineer onsite ML system design question: design, optimize and evaluate a production Retrieval-Augmented Generation (RAG) system that answers questions over a private corpus with citations. The merged answer covers the full pipeline — ingestion and layout-aware parsing, structure-aware chunking, embeddings and versioning, hybrid BM25 plus vector retrieval with RRF fusion, cross-encoder reranking, grounded generation with forced citations, abstention and a fallback ladder, ACL enforcement during retrieval, and the insert/edit/delete freshness lifecycle. It then gives a stage-by-stage evaluation plan (chunk answerability, hit-rate@K, recall@K, nDCG, faithfulness, citation quality, end-to-end success), how to bootstrap labels with no gold set, and follow-ups on debugging a wrongly cited document, embedding-model upgrades, multi-hop versus list-all queries, near-duplicates, conflicting sources and validating an LLM judge.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Select 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
• OracleMedium·Software Engineer
  Design 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
• OracleHard·Software Engineer
  Design 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
• Palo Alto NetworksHard·Software Engineer
  Design 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
• PayPalHard·Machine Learning Engineer
  Design 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
• PerplexityMedium·Software Engineer
  Design 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
• PerplexityMedium·Software Engineer
  Design 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
• PinterestMedium·Machine Learning Engineer
  Design 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
• PinterestHard·Software Engineer
  Design 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
• PinterestMedium·Machine Learning Engineer
  Design 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
• PinterestHard·Machine Learning Engineer
  Design 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
• PinterestHard·Machine Learning Engineer
  Design 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
• PinterestMedium·Machine Learning Engineer
  Design 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
• PinterestMedium·Machine Learning Engineer
  Rank 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
• PlaidMedium·Machine Learning Engineer
  Design 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
• RedditMedium·Machine Learning Engineer
  Design 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
• RedditMedium·Machine Learning Engineer
  Design 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
• RedditHard·Machine Learning Engineer
  Design 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