ML System Design interview questions

Showing 1–30 of 332 results

• Compare Single-Agent and Multi-Agent DesignsMedium

  Compare single-agent and multi-agent systems through task independence, specialization, review isolation, coordination cost, shared-state control, and evidence-based integration.

  AMDSoftware EngineerML System DesignOnsite
• Evaluate Agents That Generate or Optimize Kernels and Compiler CodeMedium

  Evaluate kernel and compiler agents with bounded transformation spaces, isolated execution, differential correctness checks, numerical tolerances, representative benchmarks, and reviewable evidence.

  AMDSoftware EngineerML System DesignOnsite
• Trace an LLM Request Through a Paged-KV Inference EngineMedium

  Trace LLM inference from request admission through prefill and decoding, explaining paged KV block tables, continuous batching, cache lifetime, and latency tradeoffs.

  AMDSoftware EngineerML System DesignOnsite
• Design a multimodal embedding serviceHard

  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.

  AdobeSoftware EngineerML System DesignTechnical Screen
• Design a natural-language AEP Q&A assistantHard

  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.

  AdobeMachine Learning EngineerML System DesignOnsite
• Design file-embedding storage systemHard

  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.

  AdobeSoftware EngineerML System DesignTechnical Screen
• Optimize LLM Training and ServingHard

  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.

  AdobeMachine Learning EngineerML System DesignOnsite
• Design User Embedding Semantic SearchMedium

  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.

  AirbnbMachine Learning EngineerML System DesignOnsite
• Design a Family-Friendly Listing ClassifierMedium

  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.

  AirbnbMachine Learning EngineerML System DesignOnsite
• Design a dynamic rental pricing systemHard

  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.

  AirbnbSoftware EngineerML System DesignOnsite
• Generate Customer-Support Instructions from Company PoliciesMedium

  Design a policy-grounded support RAG system with ordered instructions, applicability checks, procedural evaluation, citations, and version-aware debugging.

  AirbnbMachine Learning EngineerML System DesignOnsite
• Rank Home Search Results Without a Text QueryMedium

  Design a home-search ranker without query text, covering structured intent, training bias, cold start, online evaluation, and production debugging.

  AirbnbMachine Learning EngineerML System DesignOnsite
• Design NL-to-Formula assistant for AirtableMedium

  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.

  AirtableSoftware EngineerML System DesignOnsite
• Build an end-to-end ML pipelineHard

  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.

  AmazonApplied ScientistML System DesignOnsite
• Deep-dive your GenAI project architectureHard

  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.

  AmazonApplied ScientistML System DesignOnsite
• Design a Multimodal Neural NetworkHard

  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.

  AmazonApplied ScientistML System DesignOnsite
• Design a Platform for Training and Serving ML ModelsMedium

  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.

  AmazonSoftware EngineerML System DesignOnsite
• Design a RAG system end to endHard

  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.

  AmazonApplied ScientistML System DesignTechnical Screen
• Design a Reliable AI Chatbot ArchitectureEasy

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design a computer-use agent end-to-endMedium

  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.

  AmazonApplied ScientistML System DesignOnsite
• Design a fraud detection systemHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design an LLM quality validation systemMedium

  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.

  AmazonApplied ScientistML System DesignOnsite
• Design an e-commerce recommendation systemHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design an email spam detection systemHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Design systems for global request detection and labelingHard

  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.

  AmazonApplied ScientistML System DesignOnsite
• Explain ML statistics and model design conceptsHard

  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.

  AmazonApplied ScientistML System DesignTechnical Screen
• Explain parallelism and collectives in trainingMedium

  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.

  AmazonApplied ScientistML System DesignOnsite
• Optimize attention for long sequencesHard

  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.

  AmazonSoftware EngineerML System DesignTechnical Screen
• Predict Click Probability for Music RecommendationsMedium

  Design music recommendation CTR prediction with song embeddings, sparse-feature handling, model comparison, calibration, and online evaluation.

  AmazonMachine Learning EngineerML System DesignTechnical Screen
• Design an ICD-10 Prediction System for Clinical RecordsHard

  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.

  AmbienceMachine Learning EngineerML System DesignTechnical Screen