ML System Design Interview Questions

Showing 91–120 of 138 results

• OpenAIMedium·Software Engineer
  Design a Text-to-Video Generation Service

  This question tests the ability to design a scalable, reliable asynchronous inference pipeline for resource-intensive generative models, focusing on task orchestration, GPU scheduling, and latency management.

  ML System DesignTechnical ScreenML-SystemsDistributed-SystemsGPU-Orchestration
• OpenAIMedium·Software Engineer
  Design a Video Generation Service with GPU Fleet Management

  Tests the ability to design an end-to-end distributed ML inference system for compute-intensive video generation, focusing on GPU scheduling, job lifecycle management, fault tolerance, and cost efficiency.

  ML System DesignTechnical ScreenGPU SchedulingDistributed SystemsFault Tolerance
• OpenAIMedium·Software Engineer
  Design a chatbot (system design)

  This question evaluates system design skills for ML-powered chatbots with a front-end focus, testing understanding of client-side privacy and storage lifetimes, streamed response delivery, stateless relay architectures, authentication boundaries, rate limiting, and failure-mode handling.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design a low-latency RAG system

  This question evaluates ML system design skills, specifically the ability to architect retrieval-augmented generation pipelines under strict latency, cost, and multi-tenancy constraints. It tests how candidates reason about vector search, re-ranking, and LLM integration at production scale — a core competency for senior machine learning engineering roles.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design a production RAG system

  An OpenAI ML system design interview question (MLE technical screen): design a production, multi-tenant retrieval-augmented generation (RAG) system for enterprise document QA. The model answer covers ingestion and chunking, embedding selection, ANN/vector indexing (HNSW vs IVF-PQ), hybrid retrieval and cross-encoder re-ranking, prompt orchestration with citation grounding, hallucination mitigation, caching and freshness, multilingual and PII/safety handling, scalability and disaster recovery, observability and offline/online evaluation, an API and data schema, cost controls, and a phased rollout. It tests trade-off reasoning across recall, latency, cost, and compliance.

  ML System DesignTechnical Screen
• OpenAIHard·Software Engineer
  Design a response-ranking ML system

  Design a response-ranking ML 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
• OpenAIHard·Machine Learning Engineer
  Design an ML search system

  This question evaluates system-design and information-retrieval competencies for machine-learning-powered enterprise document search, covering ingestion and indexing pipelines, hybrid lexical and semantic retrieval, feature generation and ranking, multi-tenant access control, and security/compliance.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design an ML search system with RAG

  Design an ML search system with RAG 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
• OpenAIMedium·Machine Learning Engineer
  Design an OOD detection system

  This question tests the ability to design an end-to-end system for detecting out-of-distribution (OOD) data in production LLMs to ensure reliability and safety.

  ML System DesignTechnical ScreenLLMMLOpsOOD
• OpenAIHard·Software Engineer
  Design an enterprise RAG assistant for internal docs

  This question evaluates expertise in designing and training Retrieval-Augmented Generation (RAG) systems, including retriever, evaluator (reranker/verifier/filter), and generator components, with emphasis on model architecture choices, training objectives, data preparation under privacy and document-permission constraints, and evaluation strategies for grounded answers with citations. It is commonly asked to probe advanced ML system design and operationalization skills for mitigating hallucination, stale or conflicting sources, and long-document retrieval; the category is ML System Design and the level is practical application-focused with detailed modeling and training considerations.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design 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
• OracleHard·Software Engineer
  Evaluate 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
• PerplexityHard·Software Engineer
  Implement 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
• PinterestHard·Machine Learning Engineer
  Design 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
• PlaidMedium·Machine Learning Engineer
  Design 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
• RedditMedium·Machine Learning Engineer
  Design 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
• RedditHard·Software Engineer
  Design 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
• RobloxEasy·Software Engineer
  Design 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
• RobloxHard·Machine Learning Engineer
  Design 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
• RRunwayHard·Machine Learning Engineer
  Design 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
• SalesforceMedium·Software Engineer
  Design a recommendation system

  This problem tests the ability to design an end-to-end recommendation engine, focusing on the pipeline from data ingestion to real-time inference and model evaluation.

  ML System DesignTechnical ScreenRecommendation SystemsScalabilityLatency
• SalesforceHard·Software Engineer
  Design an end-to-end recommendation system

  Design an end-to-end 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
• SnapchatHard·Machine Learning Engineer
  Design 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
• SnapchatMedium·Machine Learning Engineer
  Design 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
• SnapchatMedium·Machine Learning Engineer
  Design 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
• StripeMedium·Machine Learning Engineer
  Design payment fraud detection

  This question evaluates a candidate's ability to design end-to-end machine learning systems for payment fraud detection, covering competencies in labeling, imbalanced learning, feature engineering across users/merchants/cards/devices/IPs and graph relationships, model selection, online serving, latency and feature freshness, monitoring, experimentation, and adversarial robustness. As an ML System Design problem for a Machine Learning Engineer role, it is commonly asked to probe trade-offs between fraud loss, false positives, user friction and latency and requires both conceptual understanding and practical application of production ML concerns such as evaluation metrics, thresholding, retraining, and operational constraints.

  ML System DesignTechnical Screen
• TeslaHard·Machine Learning Engineer
  Model other agents in simulation

  This question evaluates a candidate's competency in modeling other agents within simulation environments, focusing on designing agent behavior models and assessing their impact on training and evaluation robustness.

  ML System DesignTechnical Screen
• TikTokHard·Machine Learning Engineer
  Design query generation to maximize CTR

  Design query generation to maximize CTR 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
• TikTokMedium·Software Engineer
  Design system to detect privacy-leak records

  This question evaluates a candidate's ability to design scalable ML-driven systems for detecting and classifying privacy-sensitive and PII-containing records across structured and unstructured data, testing competencies in data engineering, machine learning (including deep learning and LLM/RAG integrations), system architecture, and privacy/security considerations. It is commonly asked to probe reasoning about functional and non-functional requirements, trade-offs in detection and classification approaches, evaluation metrics like precision and recall, feedback loops and operational scaling, and it falls under ML system design with a practical, application-level focus rather than purely conceptual abstraction.

  ML System DesignTechnical Screen
• TikTokMedium·Machine Learning Engineer
  Design video captioning under compute limits

  This question evaluates expertise in ML system design for multimodal large models, covering deployment under compute and GPU memory constraints as well as large-scale retrieval and processing of video captions and embeddings.

  ML System DesignTechnical Screen