Machine Learning Engineer Interview Questions

Showing 91–120 of 178 results

• MicrosoftMedium·Machine Learning Engineer
  Design a Product Search System

  This question evaluates skills in designing scalable, low-latency product search systems, covering information retrieval, ranking model design, feature generation, data ingestion and indexing pipelines, and serving infrastructure.

  ML System DesignOnsite
• MicrosoftMedium·Machine Learning Engineer
  Design a RAG-based assistant service

  This question assesses the ability to architect a scalable Retrieval-Augmented Generation (RAG) system, focusing on data indexing, retrieval strategy, and mitigating common LLM issues like hallucinations and safety.

  ML System DesignOnsiteRAGLLMVector Databases
• MicrosoftMedium·Machine Learning Engineer
  Design a Retrieval-Grounded Local Sports News Recommender

  This question tests the ability to design an end-to-end RAG system for news, focusing on temporal data relevance, hybrid retrieval strategies, and mitigation of LLM hallucinations.

  ML System DesignHR ScreenRAGRecommendationSystem Design
• MicrosoftMedium·Machine Learning Engineer
  Design email ranking and summarization in Outlook

  This problem tests the ability to architect a scalable ML pipeline that balances personalized email prioritization with LLM-based summarization while addressing low-latency requirements and data safety.

  ML System DesignOnsiteRankingGenerative AISystem Design
• MicrosoftMedium·Machine Learning Engineer
  Design quality checks for spreadsheet LLM data

  This problem tests the ability to design an end-to-end data validation pipeline for spreadsheet data and determine the necessity of fine-tuning a Large Language Model (LLM) for specific structured data tasks.

  ML System DesignTechnical ScreenLLMData ValidationFine-tuning
• MicrosoftEasy·Machine Learning Engineer
  Explain Transformers and deploy an LLM safely

  This question evaluates understanding of Transformer architectures and practical LLM deployment competencies, covering attention mechanisms, token and positional representations, computational complexity, and production concerns like latency, cost, quality, safety, and privacy.

  ML System DesignTechnical Screen
• Mistral AIMedium·Machine Learning Engineer
  Build a Small Agent or RAG Tool with the Mistral API

  Design an end-to-end Retrieval-Augmented Generation (RAG) agent using the Mistral API to answer technical queries over internal developer documentation. The question evaluates chunking strategies, dense retrieval, prompt orchestration, latency optimizations, and evaluation methodologies.

  ML System DesignTechnical Screenragml-system-designllm-agents
• NVIDIAHard·Machine Learning Engineer
  Discuss Transformer LLM Design

  Discuss Transformer LLM Design 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
• NetflixEasy·Machine Learning Engineer
  Design an ML Platform Portal

  This question assesses the ability to design an internal ML Platform Portal that streamlines the model lifecycle, focusing on experiment tracking, model registry, and workflow automation.

  ML System DesignOnsiteMLOpsSystem DesignScalability
• NetflixHard·Machine Learning Engineer
  Review a Feature Pipeline PR for Production Risks, Then Serve Features by Freshness

  Review a feature pipeline pull request whose tests all pass and explain what could break once it runs in production, from time leakage and training-serving skew to data quality and scale. Then decide how the same features should be served when the model needs day-old, minute-fresh or request-time values.

  ML System DesignTechnical Screen
• NextdoorMedium·Machine Learning Engineer
  Build an end-to-end ML classification pipeline

  This question evaluates a candidate's competency in building end-to-end tabular classification pipelines, including data loading and splitting, missing-value handling, categorical encoding, feature scaling, model training and comparison, hyperparameter tuning, metric-based evaluation, model persistence, and batch inference.

  ML System DesignTechnical Screen
• NuroHard·Machine Learning Engineer
  Design an Inference Pipeline

  This question evaluates competency in designing production machine-learning inference pipelines, covering model routing, artifact versioning and deployment, feature retrieval at inference time, low-latency/high-availability architectures, monitoring for model quality and data drift, and safe rollout strategies.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design LLM search handling long token inputs

  Design an LLM search and RAG system that handles long documents and long user queries. Covers chunking, hierarchical retrieval, query compression, context budgeting, citations, latency, and evaluation.

  ML System DesignOnsite
• OpenAIMedium·Machine Learning Engineer
  Design a Real-Time Sensor Intelligence System

  This question evaluates proficiency in end-to-end machine learning system design for real-time sensor-driven products, covering sensor choice trade-offs, data collection and labeling pipelines, model selection and real-time inference, and operational constraints such as latency, reliability, privacy, efficiency, and power.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a chatbot fallback for unknown questions

  This question evaluates a candidate's competency in ML system design, focusing on handling model uncertainty, retrieval-augmented components, tool integration, decision policies, and operational evaluation within the ML System Design domain.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a harmful video content moderation system

  This question evaluates a candidate's competency in designing scalable, multi-modal machine learning systems for content moderation, covering architecture, model strategy, labeling and human-in-the-loop workflows, metrics, auditability, and defenses against adversarial evasion.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design a low-latency RAG system

  This question evaluates ML system design skills, specifically the ability to architect retrieval-augmented generation pipelines under strict latency, cost, and multi-tenancy constraints. It tests how candidates reason about vector search, re-ranking, and LLM integration at production scale — a core competency for senior machine learning engineering roles.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design a production RAG system

  An OpenAI ML system design interview question (MLE technical screen): design a production, multi-tenant retrieval-augmented generation (RAG) system for enterprise document QA. The model answer covers ingestion and chunking, embedding selection, ANN/vector indexing (HNSW vs IVF-PQ), hybrid retrieval and cross-encoder re-ranking, prompt orchestration with citation grounding, hallucination mitigation, caching and freshness, multilingual and PII/safety handling, scalability and disaster recovery, observability and offline/online evaluation, an API and data schema, cost controls, and a phased rollout. It tests trade-off reasoning across recall, latency, cost, and compliance.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design a recommendation system end-to-end

  This question evaluates a candidate's competency in large-scale machine learning system design, including architecture for candidate generation and ranking, feature engineering across real-time and batch pipelines, training and serving workflows, and monitoring for fairness and safety.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design an AWS fine-tuning platform for LLMs

  This question evaluates a candidate's competency in designing cloud-native ML platforms for fine-tuning large language models, covering distributed training, multi-tenant security and isolation, cost controls, reproducibility, observability, checkpointing, and deployment workflows.

  ML System DesignOnsite
• OpenAIMedium·Machine Learning Engineer
  Design an Agent Harness and Evaluation System

  Design a safe harness and evaluation platform for a tool-using AI agent. Address isolated execution, repeatable task sets, trajectory tracing, deterministic and rubric-based graders, repeated trials, privacy, regression gates, and reward-hacking risks.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design an ML search system

  This question evaluates system-design and information-retrieval competencies for machine-learning-powered enterprise document search, covering ingestion and indexing pipelines, hybrid lexical and semantic retrieval, feature generation and ranking, multi-tenant access control, and security/compliance.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Design an ML search system with RAG

  Design an ML search system with RAG 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
• OpenAIMedium·Machine Learning Engineer
  Design an OOD detection system

  This question tests the ability to design an end-to-end system for detecting out-of-distribution (OOD) data in production LLMs to ensure reliability and safety.

  ML System DesignTechnical ScreenLLMMLOpsOOD
• OpenAIHard·Machine Learning Engineer
  Design an image/video near-duplicate detection system

  This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design and optimize a RAG system

  An OpenAI machine learning engineer onsite ML system design question: design, optimize and evaluate a production Retrieval-Augmented Generation (RAG) system that answers questions over a private corpus with citations. The merged answer covers the full pipeline — ingestion and layout-aware parsing, structure-aware chunking, embeddings and versioning, hybrid BM25 plus vector retrieval with RRF fusion, cross-encoder reranking, grounded generation with forced citations, abstention and a fallback ladder, ACL enforcement during retrieval, and the insert/edit/delete freshness lifecycle. It then gives a stage-by-stage evaluation plan (chunk answerability, hit-rate@K, recall@K, nDCG, faithfulness, citation quality, end-to-end success), how to bootstrap labels with no gold set, and follow-ups on debugging a wrongly cited document, embedding-model upgrades, multi-hop versus list-all queries, near-duplicates, conflicting sources and validating an LLM judge.

  ML System DesignOnsite
• OpenAIHard·Machine Learning Engineer
  Design enterprise RAG search 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 enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  ML System DesignTechnical Screen
• OpenAIHard·Machine Learning Engineer
  Select high-quality math documents from crawls

  This question assesses the ability to design a scalable pipeline that ingests, cleans, extracts, and filters high-quality mathematical content from diverse, noisy web-crawled sources.

  ML System DesignOnsiteSystem DesignNLPData Engineering
• PayPalHard·Machine Learning Engineer
  Design a traditional fraud detection system

  This question evaluates a Machine Learning Engineer's competency in end-to-end ML system design for real-time payments fraud detection, including labeling under delayed confirmations, handling extreme class imbalance and sampling, feature engineering across behavioral, graph, device and merchant signals, model selection for latency and scale, and production scoring and monitoring architecture. It is commonly asked in the ML System Design category to assess how an engineer balances low-latency decision-making with delayed sparse labels, calibration and threshold trade-offs, operational scalability and resiliency, and drift/adversarial detection, testing both conceptual understanding and practical application.

  ML System DesignOnsite
• PinterestMedium·Machine Learning Engineer
  Design Detection Systems for Risk and Safety

  This question tests the ability to design an end-to-end classification system for detecting harmful content (e.g., spam or toxic images) while balancing precision and recall at scale.

  ML System DesignOnsiteComputer VisionNLPContent Moderation