ML System Design interview questions

Showing 121–150 of 332 results

• Design a Music Recommendation System with an LLM AgentMedium

  Design a large-scale music recommendation system combining traditional multi-stage ranking with an LLM agent for natural language intent parsing and contextual explanations.

  GoogleSoftware EngineerML System DesignTechnical Screen
• Design a Product or Video Recommendation SystemMedium

  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.

  GoogleMachine Learning EngineerML System DesignTechnical Screen
• Design a Spam Review Workflow with Classifiers, AI Agents, and Human LabelersHard

  Design a spam review workflow combining classifiers, AI agents, and human labelers with accuracy, throughput, and label-quality evaluation.

  GoogleData AnalystML System DesignOnsite
• Design a reaction-factor prediction systemHard

  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.

  GoogleMachine Learning EngineerML System DesignTechnical Screen
• Design a real-time recommendation systemHard

  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.

  GoogleMachine Learning EngineerML System DesignOnsite
• Design a watch-next video recommender and derive its losses and optimizer update rulesMedium

  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.

  GoogleMachine Learning EngineerML System DesignOnsite
• Design an Agent for Internal Document SearchMedium

  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.

  GoogleSoftware EngineerML System DesignTechnical Screen
• Design an LLM Agent for Manufacturing: Production Status, Work Orders and QuotesMedium

  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.

  GoogleSoftware EngineerML System DesignTechnical Screen
• Design anomaly detection and response platformHard

  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.

  GoogleSoftware EngineerML System DesignTechnical Screen
• Design feedback-driven recommenderHard

  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.

  GoogleMachine Learning EngineerML System DesignOnsite
• Design multi-GPU matrix multiplicationHard

  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.

  GoogleMachine Learning EngineerML System DesignTechnical Screen
• Explain ML model fundamentalsHard

  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.

  GoogleMachine Learning EngineerML System DesignOnsite
• Explain Pipeline Parallelism in GRPO TrainingHard

  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.

  GoogleMachine Learning EngineerML System DesignTechnical Screen
• Build AI chat for spreadsheetsMedium

  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.

  GrammarlySoftware EngineerML System DesignOnsite
• Design Retrieval-Augmented Answers from a Document VaultHard

  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.

  HarveySoftware EngineerML System DesignOnsite
• Design a Memo Q&A Agent for a Large Law FirmMedium

  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.

  HarveySoftware EngineerML System DesignOnsite
• Design a RAG Q&A Agent over Law Firm Legal MemosHard

  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.

  HarveySoftware EngineerML System DesignOnsite
• Design a Rule-Guided Document Evaluation AgentMedium

  Design an enterprise-grade document evaluation agent that evaluates legal and policy compliance against structured playbooks, producing traceable citations, revision proposals, and tier ratings.

  HarveyAI AgentML System DesignOnsite
• Design an LLM Agent System That Revises a Contract to Comply with Dozens of RulesMedium

  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.

  HarveySoftware EngineerML System DesignOnsite
• Design and Evaluate a Retrieval-Augmented Generation PipelineMedium

  Design a RAG pipeline with document chunking, retrieval recall evaluation, grounded answers, permission-aware context, and versioned index updates.

  HarveySoftware EngineerML System DesignOnsite
• Design Hebbia Chat for SEC FilingsMedium

  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.

  HebbiaSoftware EngineerML System DesignTechnical Screen
• Design a Multi-Agent Document Search and Visualization ProductHard

  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.

  HebbiaMachine Learning EngineerML System DesignOnsite
• Design a Synchronous Text Classification APIEasy

  This question tests the ability to architect a robust, scalable, and maintainable synchronous ML inference service, focusing on operational reliability and multi-tenant management.

  Hive.AiSoftware EngineerML System DesignTechnical Screen
• Add an LLM-backed recommendation API to an existing service with RAG and promptingMedium

  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.

  IntuitSoftware EngineerML System DesignOnsite
• Design Premium Product RecommendationsEasy

  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.

  IntuitSoftware EngineerML System DesignTechnical Screen
• Design a "Future Best-Sellers" Prediction and Recommendation SystemMedium

  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.

  IntuitSoftware EngineerML System DesignTechnical Screen
• Design a Multimodal Retrieval-Augmented Generation SystemMedium

  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.

  J.P. MorganSoftware Engineer IIML System DesignOnsite
• Distinguish AI Skills, Agents, LangChain, and LangGraphMedium

  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.

  J.P. MorganSoftware Engineer IIML System DesignOnsite
• Design an end-to-end training frameworkHard

  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.

  Jane StreetMachine Learning EngineerML System DesignTechnical Screen
• Predict future time-series valuesHard

  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.

  Jane StreetMachine Learning EngineerML System DesignTechnical Screen