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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.

9403
Questions
546
Companies
11
Topics
8
Languages

Showing 181–186 of 186 results

  • Walmart LabsEasyMachine Learning Engineer
    Design an Agentic Search Experience

    Design an agentic search experience with clarifying dialogue, structured constraints, grounded retrieval, bounded tool use, and evaluation against conventional search.

    ML System DesignOnsite
  • Walmart LabsEasyMachine Learning Engineer
    Design an item category prediction system

    This question tests the ability to design an end-to-end classification system for assigning retail items into a hierarchical category structure, focusing on feature selection, model architecture, and production deployment.

    ML System DesignOnsiteClassificationNLPMulti-modal
  • WayfairMediumApplied Scientist
    Rank Homepage Modules for an E-Commerce Product

    Rank e-commerce homepage modules using business objectives, seasonal eligibility, slot effects, slate diversity, cold-start exploration, and page-level experiments.

    ML System DesignOnsite
  • WaymoMediumSoftware Engineer
    Design a Hybrid Evaluation Platform

    This problem tests your ability to design a multi-modal evaluation infrastructure that balances automated LLM-based grading with human-in-the-loop (HITL) verification to ensure high-fidelity ML model performance metrics.

    ML System DesignOnsiteMLOpsData InfrastructureHuman-in-the-loop
  • WorkdayMediumSoftware Engineer
    Design AI-Powered Document Search

    This question assesses the ability to design an end-to-end, scalable pipeline for ingesting, processing, and indexing heterogeneous documents (PDFs, images, text files) to support AI-driven semantic search.

    ML System DesignOnsiteSystem DesignML EngineeringPipelines
  • xAIMediumSoftware Engineer
    Design a Retrieval-Augmented Generation (RAG) System

    This question evaluates understanding and engineering competency for retrieval-augmented generation (RAG) systems, covering offline indexing and online query planes, retrieval quality, prompt assembly, grounded generation with citations, and production monitoring; the domain is ML System Design.

    ML System DesignOnsite