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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 1–30 of 332 results
- AMDMediumSoftware EngineerCompare Single-Agent and Multi-Agent Designs
Compare single-agent and multi-agent systems through task independence, specialization, review isolation, coordination cost, shared-state control, and evidence-based integration.
ML System DesignOnsite - AMDMediumSoftware EngineerEvaluate Agents That Generate or Optimize Kernels and Compiler Code
Evaluate kernel and compiler agents with bounded transformation spaces, isolated execution, differential correctness checks, numerical tolerances, representative benchmarks, and reviewable evidence.
ML System DesignOnsite - AMDMediumSoftware EngineerTrace an LLM Request Through a Paged-KV Inference Engine
Trace LLM inference from request admission through prefill and decoding, explaining paged KV block tables, continuous batching, cache lifetime, and latency tradeoffs.
ML System DesignOnsite - AdobeHardSoftware EngineerDesign a multimodal embedding service
This question evaluates a candidate's competency in ML system design for building scalable, multi‑tenant, privacy‑sensitive multimodal embedding pipelines, covering ingestion and idempotency, modality-specific preprocessing, model selection and fusion, throughput engineering, storage and versioning, data hygiene, and operational concerns.
ML System DesignTechnical Screen - AdobeHardMachine Learning EngineerDesign a natural-language AEP Q&A assistant
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 Design a natural-language AEP Q&A assistant states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AdobeHardSoftware EngineerDesign file-embedding storage system
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 Design file-embedding storage system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - AdobeHardMachine Learning EngineerOptimize LLM Training and Serving
This question tests the ability to diagnose hardware utilization bottlenecks in LLM workloads and design architectural optimizations to improve throughput and latency under constrained GPU memory resources.
ML System DesignOnsiteLLMGPU-ArchitecturePerformance-Engineering - AirbnbMediumMachine Learning EngineerDesign User Embedding Semantic Search
This question evaluates competency in ML system design for recommendation and semantic retrieval, covering user and listing representation learning, two-stage retrieval and ranking architectures, training objectives and label design, bias correction from interaction logs, and evaluation and experimentation practices.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerDesign a Family-Friendly Listing Classifier
This question evaluates proficiency in machine learning system design, including problem formulation tied to product objectives, labeling strategy, feature engineering, model selection, evaluation metrics, robustness to adversarial labels, fairness considerations, and cold-start handling.
ML System DesignOnsite - AirbnbHardSoftware EngineerDesign a dynamic rental pricing system
This question evaluates skills in production machine learning system design for pricing, including problem formulation, data and feature engineering, modeling and uncertainty management, online inference and scalability, experimentation and guardrails, and considerations like fairness and cold-start handling.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerGenerate Customer-Support Instructions from Company Policies
Design a policy-grounded support RAG system with ordered instructions, applicability checks, procedural evaluation, citations, and version-aware debugging.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerRank Home Search Results Without a Text Query
Design a home-search ranker without query text, covering structured intent, training bias, cold start, online evaluation, and production debugging.
ML System DesignOnsite - AirtableMediumSoftware EngineerDesign NL-to-Formula assistant for Airtable
Airtable software-engineer onsite ML system design question: design an assistant that turns natural-language requests into the product's formula language, grounded in the live table schema, and optionally applies them to the base via the Airtable API. It tests LLM orchestration with structured outputs / a typed IR, schema grounding and type checking, ambiguity handling, static + dynamic formula validation, prompt-injection safety, confirmation-gated writes, latency trade-offs, and offline/online evaluation.
ML System DesignOnsite - AmazonHardApplied ScientistBuild an end-to-end ML pipeline
Build an end-to-end ML pipeline 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 - AmazonHardApplied ScientistDeep-dive your GenAI project architecture
Deep-dive your GenAI project architecture 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 - AmazonHardApplied ScientistDesign a Multimodal Neural Network
This question evaluates a candidate's competency in designing production-grade multimodal machine learning systems, including architecture choices for text and image encoders, cross-modal fusion strategies, training objectives for joint retrieval and classification, robustness to missing or noisy modalities, and considerations for scalability and low-latency serving. Commonly asked in ML system design interviews to assess both conceptual understanding and practical application of machine learning engineering principles, it falls under the ML System Design domain and probes abilities in model alignment, evaluation metrics, deployment trade-offs, and domain adaptation strategies.
ML System DesignOnsite - AmazonMediumSoftware EngineerDesign a Platform for Training and Serving ML Models
This problem tests the ability to design an end-to-end MLOps platform capable of handling the full lifecycle of machine learning models, from experimentation to production deployment.
ML System DesignOnsiteMLOpsSystem DesignScalability - AmazonHardApplied ScientistDesign a RAG system end to end
This question evaluates a candidate's ability to design a production-grade Retrieval‑Augmented Generation (RAG) system, testing competencies in scalable ML system architecture, embedding and vector retrieval strategies, prompt orchestration, freshness and latency engineering, security/access controls, and evaluation metrics.
ML System DesignTechnical Screen - AmazonEasySoftware EngineerDesign a Reliable AI Chatbot Architecture
Tests candidate's ability to design an enterprise-grade Retrieval-Augmented Generation (RAG) chatbot system featuring fine-grained authorization, hallucination guardrails, and low-latency response serving.
ML System DesignTechnical ScreenRAGLLMInformation Retrieval - AmazonMediumApplied ScientistDesign a computer-use agent end-to-end
This problem evaluates your ability to design a multimodal agent capable of interpreting UI elements and executing sequences of actions to complete user-defined tasks on a computer screen.
ML System DesignOnsiteVLMReinforcement LearningMultimodal - AmazonHardSoftware EngineerDesign a fraud detection system
This problem evaluates your ability to design a scalable, low-latency fraud detection system for an e-commerce platform, focusing on handling class imbalance, feature engineering, and the challenges of delayed feedback loops.
ML System DesignTechnical ScreenFraud DetectionScalabilityFeature Store - AmazonMediumApplied ScientistDesign an LLM quality validation system
This question evaluates an engineer's competency in ML system design for validating large language models, testing knowledge of evaluation metrics, validation scopes from internal numerics to final responses, system architecture for automated validation pipelines, and operational concerns within the ML System Design domain; it requires both conceptual architecture understanding and practical application-level thinking. It is commonly asked to assess the ability to reason about correctness, safety, regression risk, scalability, reproducibility, and monitoring trade-offs when deploying production-grade validation pipelines and to gauge familiarity with complementary evaluation methods and end-to-end data flows.
ML System DesignOnsite - AmazonHardSoftware EngineerDesign an e-commerce recommendation system
This question evaluates expertise in large-scale machine learning system design, covering competencies in recommendation algorithms, candidate generation and ranking, feature engineering and stores, low-latency serving, and operational reliability; it is categorized in the ML system design domain and requires both conceptual understanding and practical application. It is commonly asked to assess the ability to balance personalization and business metrics (CTR, CVR, revenue), address scalability and latency targets, and manage data quality, cold-start, exploration–exploitation, A/B testing, bias/fairness, and monitoring trade-offs in production.
ML System DesignTechnical Screen - AmazonHardSoftware EngineerDesign an email spam detection system
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 Design an email spam detection system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - AmazonHardApplied ScientistDesign systems for global request detection and labeling
This problem tests the architecture of a high-throughput streaming system capable of detecting anomalies in real-time, managing extreme class imbalance for labeling, and maintaining low-latency inference at a global scale.
ML System DesignOnsiteStreamingReal-time InferenceImbalanced Learning - AmazonHardApplied ScientistExplain ML statistics and model design concepts
Explain ML statistics and model design concepts 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 - AmazonMediumApplied ScientistExplain parallelism and collectives in training
This question evaluates a candidate's competency in designing scalable distributed training systems, covering parallelism strategies (data, model/tensor, pipeline), communication collectives (all-reduce, all-gather, reduce-scatter, broadcast), and tensor-level layer partitioning such as column- and row-parallel splits.
ML System DesignOnsite - AmazonHardSoftware EngineerOptimize attention for long sequences
Optimize attention for long sequences 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 - AmazonMediumMachine Learning EngineerPredict Click Probability for Music Recommendations
Design music recommendation CTR prediction with song embeddings, sparse-feature handling, model comparison, calibration, and online evaluation.
ML System DesignTechnical Screen - AmbienceHardMachine Learning EngineerDesign an ICD-10 Prediction System for Clinical Records
Design an end-to-end clinical NLP system to predict ICD-10 codes from long medical records, addressing extreme multi-label classification, long-tail code distributions, and human-in-the-loop coder feedback.
ML System DesignTechnical Screenml-system-designnlphealthcare
Adobe
Airbnb
Airtable
Amazon