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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 61–90 of 186 results
- FigmaMediumSoftware EngineerDesign an Evaluation Platform for a Design-Editing Agent
Design an automated, multimodal evaluation platform to benchmark and monitor a canvas-based design-editing agent across deterministic property updates and subjective creative directives.
ML System DesignOnsiteLLM EvaluationAgentsMultimodal AI - FireworksMediumMachine Learning EngineerRecommend Connections Through Mutual Friends and Evaluate the Ranking
Design a connection recommendation system based on mutual connections, define bounded graph affinity scores, and evaluate the ranking pipeline using metrics like NDCG with independent labels.
ML System DesignOnsiteGraph MLRecommender SystemsRanking - GigaMediumSoftware EngineerDesign an Agentic Ticket Support System
This question evaluates a candidate's ability to design ML-driven production systems that combine agentic AI, browser-based automation for services without an API, and external user-data APIs to produce personalized support responses.
ML System DesignOnsite - Gimlet LabsMediumMember of Technical Staff, ML SystemsDesign a Text-to-Image Retrieval System
Design permission-aware text-to-image retrieval with safe ingestion, multimodal embeddings, metadata filtering, approximate search, reranking, relevance evaluation, versioned updates, deletion, and duplicate control.
ML System DesignOnsite - Gimlet LabsMediumMember of Technical Staff, ML SystemsDesign an Offline Inference Platform with Hardware-Aware Routing
Design an offline large-model inference platform that routes prefill and decode across profiled hardware pools while balancing deadlines, fairness, batching, transfer cost, retries, and utilization.
ML System DesignOnsite - GleanMediumMachine Learning EngineerDesign Employee-to-Employee Distance
This question evaluates competencies in ML system design, similarity and representation modeling, feature and data engineering, scalable low-latency serving architectures, and privacy, fairness, and evaluation practices.
ML System DesignOnsite - GleanMediumMachine Learning EngineerDesign a Work-Together Distance Between Any Two Employees of a Company
Design a learned distance between any two employees of a company that reflects how much they actually work together. Tests ML framing without ground-truth labels, collaboration signals and their normalization, link-prediction training, evaluation, privacy and serving at company scale.
ML System DesignOnsite - GofundmeMediumData ScientistExplain an AI Agent You Built: Design, Evaluation, and Governance
Practice explaining an AI-agent project through design ownership, task evaluation, hallucination controls and data governance.
ML System DesignOnsite - 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 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 - 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 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 - 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 - 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 - 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 - 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 - KikoffMediumSoftware EngineerDesign a Credit Scoring Platform
This question assesses the ability to design an end-to-end machine learning system for real-time credit risk assessment, focusing on data pipelines, model inference, and regulatory compliance.
ML System DesignOnsiteScalabilityFeature StoreCompliance - LilaMediumMachine Learning EngineerDesign a DNA-sequence optimization loop
This problem tests your ability to design a closed-loop active learning system for protein or DNA sequence engineering, focusing on the iterative cycle of sequence generation, uncertainty estimation, and experimental validation.
ML System DesignOnsiteActive LearningGenerative ModelsBioinformatics - LinkedInMediumMachine Learning EngineerDesign LinkedIn Learning course recommendations
The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.
ML System DesignOnsite - LinkedInMediumMachine Learning EngineerDesign a Skills inference system
This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.
ML System DesignOnsiteNLPEntity LinkingRecommendation Systems - LinkedInMediumMachine Learning EngineerDesign a system for LinkedIn Skills
This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.
ML System DesignOnsite - MercorMediumMachine Learning EngineerBuild a Candidate Search System
Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.
ML System DesignOnsiteml system designinformation retrievalnlp
Figma
Glean
Google
Grammarly
Harvey
Intuit
J.P. Morgan
LinkedIn
Mercor