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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 31–60 of 138 results
- Capital OneMediumData ScientistExplain an ML Project from Model Development Through Deployment
This question assesses your ability to articulate the end-to-end lifecycle of an ML project, focusing on the bridge between technical modeling choices and real-world business impact.
ML System DesignTechnical ScreenML LifecycleModel DeploymentSystem Design - CitadelMediumData ScientistStabilize LLM inference and estimate needed repeats
This question tests the ability to design an LLM inference pipeline that ensures result stability through statistical aggregation and estimates the minimum number of inference samples required to achieve a target confidence level.
ML System DesignTechnical ScreenLLMInferenceStatistics - CognitivMediumMachine Learning EngineerDesign a Real-Time Feature Store
This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.
ML System DesignTechnical Screen - CreditkarmaMediumMachine Learning EngineerDesign Personalized Promotion Recommendations
This question tests the ability to design an end-to-end recommendation system for personalized financial promotions, focusing on handling sparse conversion data, real-time feature requirements, and feedback loops.
ML System DesignTechnical ScreenRecommendation SystemsRankingFeature Engineering - CriblMediumSoftware EngineerDesign an LLM Log Parsing Workflow
This question evaluates skills in ML-enabled log parsing, schema inference, structured data extraction, and designing scalable, reliable production workflows that combine probabilistic LLMs with deterministic parsers and operational engineering.
ML System DesignTechnical Screen - DatadogMediumSoftware EngineerDesign an LLM Agent System That Automatically Resolves Jira Tickets and Opens Pull Requests
Design an autonomous agent pipeline that ingests Jira bug reports, grounds itself in a multi-repo codebase via code search and retrieval, iterates on a fix in a sandboxed runtime, and opens a validated pull request.
ML System DesignTechnical ScreenLLM AgentsCode GenerationRAG - DatadogHardSoftware EngineerDesign an image detection system
This question evaluates a candidate's competence in designing scalable, reliable end-to-end machine learning systems for image object detection, including ingestion, preprocessing, model serving, data and version management, monitoring, and operational concerns.
ML System DesignTechnical Screen - DavidaiMediumSoftware EngineerDesign a Scalable Audio AI Processing Platform
Tests the candidate's ability to design a resilient, distributed ML platform for audio processing that supports both interactive and batch workloads, efficient GPU scheduling, and granular failure recovery.
ML System DesignTechnical Screenml-system-designdistributed-systemsgpu-scheduling - DecagonMediumSoftware EngineerDesign a Real-Time AI Voice System
This problem evaluates your ability to design a low-latency, real-time conversational AI system, focusing on component orchestration and performance trade-offs in speech processing pipelines.
ML System DesignTechnical ScreenSystem DesignReal-time SystemsStreaming - DisneyHardSoftware EngineerDesign a personalized recommendation system
This question evaluates a candidate's competency in ML system design and recommender systems engineering, covering data ingestion, feature stores, candidate generation, ranking, online serving, experimentation, and operational considerations.
ML System DesignTechnical Screen - DistylHardSoftware EngineerDesign a Grounded LLM Assistant for Case Preparation
Design an enterprise-grade, retrieval-grounded LLM assistant for litigation teams to summarize discovery documents and draft case briefs with strict matter isolation, citation provenance, contradiction detection, and injection guardrails.
ML System DesignTechnical ScreenRAGLLM SafetyInformation Retrieval - DoorDashHardMachine Learning EngineerDiscuss ML infrastructure fundamentals
Discuss ML infrastructure 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 DesignTechnical Screen - FigureEasySoftware EngineerDesign a Multimodal Training Data Pipeline
This question evaluates a candidate's ability to design scalable, reliable backend systems for ingesting and processing multimodal training data, testing competencies in distributed system architecture, API and storage design, large-media handling, data validation, duplicate detection, quality control, observability, and awareness of data bias.
ML System DesignTechnical Screen - Freddie MacMediumMachine Learning EngineerImprove Trust in a RAG System
This question evaluates system-design competency for trustworthy retrieval-augmented generation, probing skills in retrieval and generation architecture, confidence scoring and calibration, evidence verification and fallback mechanisms, monitoring, privacy and compliance, human feedback integration, and adversarial robustness.
ML System DesignTechnical Screen - GoogleMediumSoftware EngineerChoose Fast or Cheap Models
This question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.
ML System DesignTechnical Screen - GoogleMediumMachine Learning EngineerDesign ML system for self-driving perception
This question assesses the ability to architect a real-time perception system for autonomous vehicles, focusing on sensor integration, model optimization for edge deployment, and safety-critical failure handling.
ML System DesignTechnical ScreenComputer VisionSensor FusionEdge ML - 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 - 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 - 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 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 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 - 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 - 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 - 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 - 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
Capital One
Citadel
Datadog
Decagon
Disney
DoorDash
Freddie Mac
Google
Intuit
Jane Street