ML System Design Interview Questions

Showing 181–210 of 332 results

• MetaHard·Machine Learning Engineer
  Design image and multimodal generation systems

  This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.

  ML System DesignTechnical Screen
• MetaHard·Machine Learning Engineer
  Design nearby place recommendations

  This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.

  ML System DesignOnsite
• MetaHard·Software Engineer
  Design place-of-interest ML system

  Design place-of-interest 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 DesignOnsite
• MetaMedium·Machine Learning Engineer
  Design weapon-selling ad detection from posts

  This question tests the ability to design an end-to-end multimodal classification system for detecting policy-violating content (weapon sales) while balancing latency, precision, and robustness against adversarial evasion.

  ML System DesignOnsiteComputer VisionNLPMultimodal Learning
• MetaMedium·Software Engineer
  Explain an AI System You Designed and the Problem It Solved

  Tests your ability to articulate an end-to-end production AI/ML system you designed, detailing the business justification, technical architecture, evaluation strategy, failure modes, and your individual contributions.

  ML System DesignTechnical Screenml-system-designai-architecturemodel-evaluation
• MetaMedium·Machine Learning Engineer
  Prevent Private Code Leakage in Coding Agents

  This problem tests the ability to design an ML-driven safeguard system that prevents private codebase exposure during the training, fine-tuning, and inference phases of a coding agent.

  ML System DesignTechnical ScreenMLOpsSecurityPrivacy
• MicrosoftMedium·Machine Learning Engineer
  Calibrate LLM output to match Word formatting

  This question tests the ability to design an end-to-end system for enforcing strict structured formatting (e.g., Docx/Markdown) on LLM-generated text, focusing on constraint enforcement and reliability.

  ML System DesignOnsiteLLMSystem DesignConstrained Decoding
• MicrosoftMedium·Software Engineer
  Design Chatbot Personalization Memory

  This problem evaluates the ability to design a scalable, low-latency memory system for an LLM-based chatbot that stores and retrieves user-specific facts to provide personalized responses.

  ML System DesignOnsiteSystem DesignRAGLLM
• MicrosoftMedium·Data Scientist
  Design Product-Description Content Moderation at Marketplace Scale

  Design an ML moderation system for multilingual seller descriptions at marketplace scale. Cover policy-aware labels, severity-based actions, calibrated thresholds, human review and appeals, low-latency serving, selective rescans, auditability, drift, and adversarial abuse.

  ML System DesignTechnical Screen
• MicrosoftMedium·Machine Learning Engineer
  Design a Product Search System

  This question evaluates skills in designing scalable, low-latency product search systems, covering information retrieval, ranking model design, feature generation, data ingestion and indexing pipelines, and serving infrastructure.

  ML System DesignOnsite
• MicrosoftMedium·Software Engineer
  Design a RAG Ranking Pipeline

  This problem tests your ability to design a multi-stage retrieval and ranking architecture for a RAG system, focusing on balancing search accuracy, latency, and system scalability.

  ML System DesignOnsiteRAGInformation RetrievalSystem Design
• MicrosoftMedium·Machine Learning Engineer
  Design a RAG-based assistant service

  This question assesses the ability to architect a scalable Retrieval-Augmented Generation (RAG) system, focusing on data indexing, retrieval strategy, and mitigating common LLM issues like hallucinations and safety.

  ML System DesignOnsiteRAGLLMVector Databases
• MicrosoftMedium·Machine Learning Engineer
  Design a Retrieval-Grounded Local Sports News Recommender

  This question tests the ability to design an end-to-end RAG system for news, focusing on temporal data relevance, hybrid retrieval strategies, and mitigation of LLM hallucinations.

  ML System DesignHR ScreenRAGRecommendationSystem Design
• MicrosoftMedium·Machine Learning Engineer
  Design email ranking and summarization in Outlook

  This problem tests the ability to architect a scalable ML pipeline that balances personalized email prioritization with LLM-based summarization while addressing low-latency requirements and data safety.

  ML System DesignOnsiteRankingGenerative AISystem Design
• MicrosoftMedium·Machine Learning Engineer
  Design quality checks for spreadsheet LLM data

  This problem tests the ability to design an end-to-end data validation pipeline for spreadsheet data and determine the necessity of fine-tuning a Large Language Model (LLM) for specific structured data tasks.

  ML System DesignTechnical ScreenLLMData ValidationFine-tuning
• MicrosoftEasy·Machine Learning Engineer
  Explain Transformers and deploy an LLM safely

  This question evaluates understanding of Transformer architectures and practical LLM deployment competencies, covering attention mechanisms, token and positional representations, computational complexity, and production concerns like latency, cost, quality, safety, and privacy.

  ML System DesignTechnical Screen
• MicrosoftMedium·Applied Scientist
  Predict Employee Attrition from an HR Dataset and Explain Its Drivers

  A machine learning design exercise: given a tab-separated HR dataset with features such as age, job role, monthly income and overtime, predict which employees will leave and explain which factors drive attrition. It covers feature processing, model choice, class imbalance, evaluation metrics, threshold selection and interpretability.

  ML System DesignOnsite
• MicrosoftHard·Software Engineer
  Shard a Large Input Embedding Table Across Devices

  Compare vocabulary and hidden-dimension embedding sharding through per-rank storage, token routing, hidden-state layout, skew, and downstream communication.

  ML System DesignOnsite
• MicrosoftHard·Software Engineer
  Shard an LM Head and Combine Distributed Logits

  Shard an LM head by vocabulary or hidden width, distinguishing complete logits from partial dot products and exact distributed top-k from full-distribution sampling.

  ML System DesignOnsite
• MillenniumMedium·Data Scientist
  Design a Governed Agent for Portfolio Rebalancing

  Design an autonomous, LLM- or ML-driven portfolio rebalancing agent integrated with deterministic guardrails, pre-trade compliance checks, and audit logging. The design must guarantee safety, idempotency, strict separation of concerns, and full state reconstructibility.

  ML System DesignTechnical Screenml-system-designai-agentsrisk-management
• Mistral AIMedium·Machine Learning Engineer
  Build a Small Agent or RAG Tool with the Mistral API

  Design an end-to-end Retrieval-Augmented Generation (RAG) agent using the Mistral API to answer technical queries over internal developer documentation. The question evaluates chunking strategies, dense retrieval, prompt orchestration, latency optimizations, and evaluation methodologies.

  ML System DesignTechnical Screenragml-system-designllm-agents
• Mistral AIHard·Software Engineer
  Build and design a Mistral RAG agent

  This question evaluates a candidate's skills in building a retrieval-augmented generation (RAG) system, covering document ingestion, chunking, embedding generation, vector indexing, retrieval, LLM API-driven streaming completions, short-term conversation memory, and operational reliability features.

  ML System DesignTechnical Screen
• Mistral AIHard·Software Engineer
  Design a PDF-to-Markdown Inference API

  This question evaluates system-design and ML inference orchestration skills, assessing competency in composing multi-stage pipelines that manage heterogeneous resource profiles (CPU, GPU, memory) and trade-offs between latency and throughput.

  ML System DesignTechnical Screen
• NVIDIAMedium·Software Engineer
  Describe model-to-GPU execution pipeline

  Describe model-to-GPU execution pipeline 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
• NVIDIAMedium·Software Engineer
  Design and benchmark optimized inference pipelines

  Design and benchmark optimized inference pipelines 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
• NVIDIAEasy·Software Engineer
  Design real-time fraud detection under 50ms

  This question evaluates expertise in designing low-latency, high-throughput ML-powered systems for online decisioning, covering competencies in real-time inference, feature pipelines, model serving, scaling, and operational reliability within the ML System Design domain.

  ML System DesignTechnical Screen
• NVIDIAHard·Machine Learning Engineer
  Discuss Transformer LLM Design

  Discuss Transformer LLM Design 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
• NVIDIAMedium·Software Engineer
  Explain ML compilation optimizations and hardware fit

  This question evaluates a candidate's understanding of ML compiler optimizations and hardware-aware runtime strategies, assessing competencies in techniques such as kernel fusion, quantization, memory planning, scheduling/tiling, layout selection, sparsity, mixed precision, graph-level rewrites and runtime tactics.

  ML System DesignTechnical Screen
• NVIDIAHard·Software Engineer
  Explain ML framework trends

  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 Explain ML framework trends states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  ML System DesignTechnical Screen
• NVIDIAMedium·Software Engineer
  How would you optimize large-scale training/inference?

  This problem tests your understanding of performance bottlenecks in large-scale machine learning systems, requiring you to prioritize optimization strategies across memory, compute, and communication dimensions.

  ML System DesignTechnical ScreenML SystemsGPU ArchitecturePerformance Engineering