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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 121–150 of 332 results
- GoogleMediumSoftware EngineerDesign a Music Recommendation System with an LLM Agent
Design a large-scale music recommendation system combining traditional multi-stage ranking with an LLM agent for natural language intent parsing and contextual explanations.
ML System DesignTechnical ScreenRecommender SystemsLLM AgentsInformation Retrieval - GoogleMediumMachine Learning EngineerDesign a Product or Video Recommendation System
This question evaluates competency in designing large-scale recommendation systems, encompassing machine learning modeling, candidate generation and ranking, data engineering, online serving, feedback-driven learning, evaluation, and experimentation.
ML System DesignTechnical Screen - GoogleHardData AnalystDesign a Spam Review Workflow with Classifiers, AI Agents, and Human Labelers
Design a spam review workflow combining classifiers, AI agents, and human labelers with accuracy, throughput, and label-quality evaluation.
ML System DesignOnsite - GoogleHardMachine Learning EngineerDesign a reaction-factor prediction system
This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign a real-time recommendation system
This question evaluates a candidate's competency in designing large-scale, low-latency real-time recommendation systems, covering feature engineering and feature-store consistency, candidate generation and ranking architectures, cold-start handling, latency versus accuracy trade-offs, monitoring, and operational scalability.
ML System DesignOnsite - GoogleMediumMachine Learning EngineerDesign a watch-next video recommender and derive its losses and optimizer update rules
Design the watch-next recommendations shown while a user watches a video, from objectives and candidate generation to ranking, serving and evaluation. The interview probes modeling depth: exact loss functions for retrieval and multi-task ranking, the gradient at the output logit, and the update rules of the optimizers used to train each model.
ML System DesignOnsite - GoogleMediumSoftware EngineerDesign an Agent for Internal Document Search
Design an enterprise search agent that provides grounded, cited answers over internal knowledge repositories while strictly enforcing access control lists (ACLs) and handling prompt injection.
ML System DesignTechnical ScreenRAGSearch SystemsLLM Agents - GoogleMediumSoftware EngineerDesign an LLM Agent for Manufacturing: Production Status, Work Orders and Quotes
Design an LLM-powered agent for a manufacturing business that checks production progress, opens work orders and gives customers price quotes. Probes tool design for reads and writes, how to stop the agent from inventing statuses or prices, and how to enforce user permissions outside the model.
ML System DesignTechnical Screen - GoogleHardSoftware EngineerDesign anomaly detection and response platform
This question evaluates a candidate's competency in designing end-to-end ML-driven systems that handle large-scale, low-latency OS snapshot ingestion, binary anomaly detection, decisioning and action orchestration with multi-tenant security, auditability, and failure rollback.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign feedback-driven recommender
Design feedback-driven recommender 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 - GoogleHardMachine Learning EngineerDesign multi-GPU matrix multiplication
This question evaluates proficiency in multi-GPU parallelism and system-level ML engineering, covering data partitioning, inter-GPU communication primitives, compute scheduling and overlap, memory layout and buffer reuse, numerical precision trade-offs, synchronization, scalability, and failure handling.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerExplain ML model fundamentals
Explain ML model fundamentals 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 - GoogleHardMachine Learning EngineerExplain Pipeline Parallelism in GRPO Training
Design and analyze an efficient pipeline parallel training architecture for Group Relative Policy Optimization (GRPO) on large reasoning models. Tests deep understanding of pipeline scheduling, microbatching, distributed advantage normalization, and policy staleness trade-offs.
ML System DesignTechnical ScreenPipeline ParallelismRLHF / GRPODistributed Training - GrammarlyMediumSoftware EngineerBuild AI chat for spreadsheets
This question evaluates a candidate's ability to design ML-driven conversational interfaces and systems integration, emphasizing LLM orchestration, tool schema and API design, authorization and validation, ambiguity handling, atomic multi-step execution, safety against prompt injection, and operational concerns like latency, cost, scaling, and observability. It is commonly asked to assess architectural thinking for integrating language models with controlled tool-calling and safe execution; category: ML System Design; level of abstraction: system-level design requiring both conceptual understanding and practical application.
ML System DesignOnsite - HarveyHardSoftware EngineerDesign Retrieval-Augmented Answers from a Document Vault
Design an enterprise-grade retrieval-augmented generation (RAG) service for confidential document vaults. The question tests multi-tenant access control, precise citation provenance, document versioning/freshness, retrieval quality, and hallucination/injection defenses.
ML System DesignOnsiteRAGAccess ControlInformation Retrieval - HarveyMediumSoftware EngineerDesign a Memo Q&A Agent for a Large Law Firm
This problem evaluates your ability to design a RAG-based Q&A system for sensitive legal documents, focusing on multi-stage retrieval, hallucination prevention, and data privacy.
ML System DesignOnsiteRAGLLMSystem Design - HarveyHardSoftware EngineerDesign a RAG Q&A Agent over Law Firm Legal Memos
Design an end-to-end Retrieval-Augmented Generation (RAG) system for querying confidential, complex legal memos. This evaluates domain-specific parsing, hybrid retrieval with fine-grained access control, and grounded generation with strict citation fidelity.
ML System DesignOnsiteRAGVector SearchInformation Retrieval - HarveyMediumAI AgentDesign a Rule-Guided Document Evaluation Agent
Design an enterprise-grade document evaluation agent that evaluates legal and policy compliance against structured playbooks, producing traceable citations, revision proposals, and tier ratings.
ML System DesignOnsiteml-system-designllm-agentslegal-ai - HarveyMediumSoftware EngineerDesign an LLM Agent System That Revises a Contract to Comply with Dozens of Rules
Design an LLM agent system that revises a given contract so it complies with several dozen given legal rules. It tests decomposing work across model calls, representing edits so they can be traced and merged, resolving overlapping or conflicting changes, per-rule verification, and cost and latency trade-offs.
ML System DesignOnsite - HarveyMediumSoftware EngineerDesign and Evaluate a Retrieval-Augmented Generation Pipeline
Design a RAG pipeline with document chunking, retrieval recall evaluation, grounded answers, permission-aware context, and versioned index updates.
ML System DesignOnsite - HebbiaMediumSoftware EngineerDesign Hebbia Chat for SEC Filings
This problem tests your ability to design a retrieval-augmented generation (RAG) system specialized for financial documents, focusing on multi-agent orchestration, citations, and latency management.
ML System DesignTechnical ScreenRAGLLMSystem Design - HebbiaHardMachine Learning EngineerDesign a Multi-Agent Document Search and Visualization Product
This problem tests your ability to design a complex multi-agent orchestration system that integrates large-scale document retrieval with reliable citation tracking and a user-facing visualization interface.
ML System DesignOnsiteMulti-Agent SystemsRAGOrchestration - Hive.AiEasySoftware EngineerDesign a Synchronous Text Classification API
This question tests the ability to architect a robust, scalable, and maintainable synchronous ML inference service, focusing on operational reliability and multi-tenant management.
ML System DesignTechnical ScreenSystem DesignAPI DesignMLOps - IntuitMediumSoftware EngineerAdd an LLM-backed recommendation API to an existing service with RAG and prompting
Add an LLM-powered recommendation endpoint to an existing backend service. Design the request flow, write the actual prompt and its structured output format, and discuss retrieval-augmented generation, hallucination, prompt injection, and how to evaluate and monitor the feature.
ML System DesignOnsite - IntuitEasySoftware EngineerDesign Premium Product Recommendations
This question tests the ability to design an end-to-end recommendation architecture, focusing on the trade-offs between relevance, latency, and data privacy in a financial context.
ML System DesignTechnical ScreenSystem DesignRecommendation SystemsML Ops - IntuitMediumSoftware EngineerDesign a "Future Best-Sellers" Prediction and Recommendation System
This question tests the ability to design an end-to-end machine learning system that predicts future best-sellers and serves real-time recommendations, balancing model accuracy, batch processing, and serving latency.
ML System DesignTechnical ScreenSystem DesignMLOpsRecommendations - J.P. MorganMediumSoftware Engineer IIDesign a Multimodal Retrieval-Augmented Generation System
This problem tests the ability to design an end-to-end RAG system capable of processing both text and image inputs while ensuring high retrieval precision, security, and traceability.
ML System DesignOnsiteRAGMultimodalSystem Design - J.P. MorganMediumSoftware Engineer IIDistinguish AI Skills, Agents, LangChain, and LangGraph
This question tests the architectural understanding of modern LLM-based application frameworks and the distinctions between modular AI components, task-oriented agents, and multi-agent coordination systems.
ML System DesignOnsiteLLMOrchestrationAgentic-Workflows - Jane StreetHardMachine Learning EngineerDesign an end-to-end training framework
Design an end-to-end training framework 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 - Jane StreetHardMachine Learning EngineerPredict future time-series values
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 Predict future time-series values states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen
Google
Grammarly
Harvey
Intuit
J.P. Morgan
Jane Street