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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.
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Showing 91–120 of 186 results
- MercorMediumMachine Learning EngineerDesign Candidate Search And LLM Evaluation Tasks
Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.
ML System DesignOnsite - MercorMediumMachine Learning EngineerDesign Work Orchestration for Machine-Learning Data Pipelines
Design a resilient, multi-tenant orchestration engine tailored for machine learning pipelines that manages versioned DAGs, heterogeneous compute routing, data quality gates, and atomic dataset publication.
ML System DesignOnsiteML InfrastructureSystem DesignWorkflow Orchestration - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
ML System DesignOnsite - MetaHardSoftware EngineerBuild a Mistral-powered RAG agent
This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.
ML System DesignOnsite - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign a Code Review Agent
This question evaluates a candidate's ability to design ML-powered developer tooling, covering competencies in LLM orchestration, deterministic tool integration, repository context management, code-analysis heuristics, safety and validation strategies, and inference cost estimation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Large-Scale Personalized Recommendation System End to End
An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a newsfeed dislike model
This problem tests the ability to design an end-to-end ML system for predicting user dissatisfaction, focusing on label definition, feature engineering, and the integration of negative signals into a ranking pipeline.
ML System DesignOnsiteRecommender SystemsRankingImbalanced Learning - MetaHardMachine Learning EngineerDesign a scalable MoE pretraining pipeline
This question evaluates expertise in large-scale ML system design and distributed training for Mixture-of-Experts (MoE) models, covering competencies in model architecture, parallelism and communication strategies, memory and optimizer sharding, data curation and tokenization, monitoring, checkpointing, and fault tolerance; it is categorized under ML System Design and targets practical application with systems-level architectural reasoning rather than purely theoretical concepts. It is commonly asked to evaluate the ability to balance throughput, stability, and cost when scaling pretraining to hundreds of GPUs, reason about routing and capacity trade-offs, and design production-grade pipelines that can extend to larger clusters.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a weapon-sale ad detection system
This question tests the ability to architect an end-to-end multimodal classification pipeline for detecting policy-violating content at scale while balancing precision, recall, and operational latency.
ML System DesignOnsiteMultimodal LearningContent ModerationClassification - MetaHardMachine Learning EngineerDesign an LLM-Based Coding Assistant
Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.
ML System DesignOnsiteLLMInference ServingCode Generation - MetaHardMachine Learning EngineerDesign an LLM-Based Conversational Assistant (Chatbot)
Design an end-to-end conversational AI assistant powered by large language models, covering post-training alignment, retrieval/tool orchestration, low-latency serving, and safety guardrails at consumer scale.
ML System DesignOnsiteLLMRAGModel Serving - MetaMediumMachine Learning EngineerDesign an image copyright-violation detection system
This problem evaluates your ability to design a scalable, end-to-end computer vision pipeline capable of identifying copyrighted content within user-uploaded images, including scenarios involving cropping, filters, and collages.
ML System DesignOnsiteComputer VisionEmbeddingsSimilarity Search - MetaMediumMachine Learning EngineerDesign comment ranking for a news feed
This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.
ML System DesignOnsiteRankingRecommendation SystemsMulti-task Learning - MetaHardMachine Learning EngineerDesign 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 - MetaHardSoftware EngineerDesign 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 - MetaMediumMachine Learning EngineerDesign 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 - MicrosoftMediumMachine Learning EngineerCalibrate 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 - MicrosoftMediumSoftware EngineerDesign 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 - MicrosoftMediumMachine Learning EngineerDesign 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 - MicrosoftMediumSoftware EngineerDesign 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 - MicrosoftMediumMachine Learning EngineerDesign 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 - MicrosoftMediumMachine Learning EngineerDesign 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 - MicrosoftMediumApplied ScientistPredict 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 - MicrosoftHardSoftware EngineerShard 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 - MicrosoftHardSoftware EngineerShard 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 - NetappMediumSoftware EngineerDesign a Production Retrieval-Augmented Generation Pipeline
This problem tests the ability to design an end-to-end RAG system, focusing on document processing, embedding strategies, retrieval mechanisms, and ensuring accuracy through grounding.
ML System DesignOnsiteRAGLLMVector Database - NetflixHardSoftware EngineerBuild a Recommendation System and Conversational Interface from Scratch
Design an initial recommendation system, then extend it with a chatbot that can retrieve and explain recommendations while respecting the same eligibility and ranking rules. Connect data and model choices to serving architecture, latency and throughput, evaluation, monitoring, failure modes, and iteration.
ML System DesignOnsite - NetflixEasyMachine Learning EngineerDesign an ML Platform Portal
This question assesses the ability to design an internal ML Platform Portal that streamlines the model lifecycle, focusing on experiment tracking, model registry, and workflow automation.
ML System DesignOnsiteMLOpsSystem DesignScalability
Mercor
Meta
Microsoft
Netflix