Machine Learning Engineer Interview Questions

Showing 1–30 of 178 results

• AdobeHard·Machine Learning Engineer
  Design 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
• AdobeHard·Machine Learning Engineer
  Optimize 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
• AirbnbMedium·Machine Learning Engineer
  Design 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
• AirbnbMedium·Machine Learning Engineer
  Design 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
• AirbnbMedium·Machine Learning Engineer
  Generate 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
• AirbnbMedium·Machine Learning Engineer
  Rank 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
• AmazonMedium·Machine Learning Engineer
  Predict 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
• AAmbienceHard·Machine Learning Engineer
  Design 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
• AAmericanMedium·Machine Learning Engineer
  Design an enterprise RAG agent system

  This question evaluates a candidate's competency in ML system design for enterprise retrieval-augmented generation and agent architectures, covering understanding of model roles, agent tool integrations, memory and state design, evaluation metrics, security and privacy defenses, concurrency and reliability, workflow durability, and document ingestion/indexing challenges. It is commonly asked in ML system design interviews to assess architectural thinking and trade-off analysis, testing both conceptual understanding and practical application skills for building scalable, secure, and cost-effective enterprise AI assistants.

  ML System DesignTechnical Screen
• AnthropicMedium·Machine Learning Engineer
  Design a model downloader

  This question assesses the architectural design of a distributed, secure, and resilient system capable of deploying large-scale machine learning models to heterogeneous edge devices.

  ML System DesignOnsiteSystem DesignMLOpsDistributed Systems
• AnthropicHard·Machine Learning Engineer
  Design an inference routing and scheduling layer

  This question evaluates system-design and production machine learning engineering skills, specifically distributed systems, routing and scheduling, multi-tenant isolation, caching, dynamic batching, and capacity planning for heterogeneous GPU/CPU inference backends.

  ML System DesignOnsite
• AnthropicHard·Machine Learning Engineer
  Extend an LLM Inference Server with a Prefix KV Cache

  Anthropic Fellows OA practice: add exact-match, prefix and shared-prefix KV caching with an LRU token budget to an existing LLM inference server, keeping greedy outputs identical.

  ML System DesignOnline AssessmentAnthropic FellowsAI Safety Fellowshipllm-inference
• AnthropicHard·Machine Learning Engineer
  Schedule Large and Small Model Inference on an Eight-GPU Pool

  Design an inference scheduler for a shared 8-GPU node that co-locates single-GPU small models and all-reduce 8-GPU large models. The design must address gang scheduling, prefix cache reuse, deadline-aware priorities, and overload mitigation.

  ML System DesignTechnical Screenml-system-designgpu-schedulingdistributed-inference
• AppleMedium·Machine Learning Engineer
  Choose File Storage Tiers Using Access Predictions

  Design an end-to-end machine learning and decision system that predicts file access likelihood to automatically transition cloud-stored user files across storage tiers to minimize operational cost while meeting retrieval latency SLAs.

  ML System DesignTechnical Screenml-system-designprobability-calibrationcloud-storage
• AppleMedium·Machine Learning Engineer
  Design App Store search

  This question evaluates a candidate's ability to design scalable, low-latency app marketplace search systems, covering competencies in query understanding, metadata ingestion and indexing, candidate retrieval and ranking, personalization, multilingual and typo-tolerant handling, cold-start and spam prevention, as well as online experimentation and monitoring. Commonly asked in the ML system design/search engineering domain to probe architectural trade-offs, measurement of product requirements and success metrics, and operationalization, it spans both high-level conceptual reasoning and practical application details.

  ML System DesignOnsite
• AppleMedium·Machine Learning Engineer
  Design a CPA system for ad bidding

  This question tests the ability to design an end-to-end Cost-Per-Acquisition (CPA) bidding system, specifically focusing on how to model conversion probabilities and handle the complexities of delayed feedback and bid pacing.

  ML System DesignTechnical ScreenSystem DesignML OpsAdTech
• AppleMedium·Machine Learning Engineer
  Design a grounded voice assistant

  This problem evaluates your ability to design a retrieval-augmented generation (RAG) system for a voice assistant, focusing on grounding model outputs in trusted sources to prevent hallucinations.

  ML System DesignOnsiteRAGLLMSystem Design
• AppleHard·Machine Learning Engineer
  Design a streaming embedding-based classifier

  This question evaluates a candidate's ability to design end-to-end streaming machine learning systems, including online text preprocessing, tokenization, embedding generation, continuous model training, and low-latency classification serving.

  ML System DesignTechnical Screen
• AppleMedium·Machine Learning Engineer
  Optimize image filters on device

  This question evaluates competency in on-device machine learning and performance engineering, covering algorithm choice, numerical precision trade-offs, hardware utilization (CPU/GPU/accelerator), memory layout, tiling/streaming, and trade-offs among latency, memory overhead, and battery impact.

  ML System DesignOnsite
• Applied IntuitionMedium·Machine Learning Engineer
  Debug a GPT training pipeline

  This question evaluates debugging and implementation skills for transformer-based training pipelines, specifically attention masking, training-loop correctness, positional encoding integration, and the ability to design unit tests that catch such bugs.

  ML System DesignTechnical Screen
• AtlassianMedium·Machine Learning Engineer
  Design Jira bug-to-team classification system

  This question tests the ability to design an end-to-end ML pipeline for classifying incoming Jira tickets to appropriate engineering teams based on textual and historical metadata.

  ML System DesignOnsiteNLPClassificationScalability
• AtlassianMedium·Machine Learning Engineer
  Design a Jira+Confluence RAG assistant

  This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.

  ML System DesignOnsite
• AtlassianHard·Machine Learning Engineer
  Design an ML-powered search system

  This question tests the ability to design a scalable, low-latency, and relevant search system for internal enterprise documents, focusing on the integration of hybrid search (keyword + semantic) and ranking strategies.

  ML System DesignOnsiteInformation RetrievalRankingEmbeddings
• AtlassianMedium·Machine Learning Engineer
  Design an internal data-access chatbot

  This question evaluates a candidate's ability to design production-ready ML systems that integrate retrieval-augmented generation, secure access control, tool-based workflows, data privacy, auditability, and low-latency interaction.

  ML System DesignOnsite
• AuroraHard·Machine Learning Engineer
  Applying LLMs to Autonomous Driving Despite Hallucination and Real-Time Limits

  An open-ended ML system design discussion on how large language models and vision-language models could be used in autonomous driving, including whether one could actually drive the vehicle. It tests mapping off-board and on-vehicle uses, containing hallucination and latency in a safety-critical stack, and planning evaluation.

  ML System DesignTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Design Self-Dealing Detection for Marketplaces

  This question evaluates competency in machine learning system design for fraud detection, focusing on modeling buyer–seller–item–transaction relationships, feature engineering across graph, temporal, behavioral and account-linking signals, and production concerns; it is in the ML System Design domain and emphasizes practical application and system-level architecture over purely theoretical concepts. It is commonly asked to assess a candidate’s ability to reason about data collection and labeling, model and rule trade-offs, evaluation metrics, scalability and deployment trade-offs when detecting coordinated or self-dealing activity in marketplace environments.

  ML System DesignTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Design an Enterprise Tool-Using Agent

  This question evaluates a candidate's ability to design production-grade LLM agents that integrate external tools, manage long-running and branching workflows, persist complex state, and ensure safety, observability, and reliable evaluation in enterprise settings.

  ML System DesignTechnical Screen
• Capital OneMedium·Machine Learning Engineer
  Design model deployment, monitoring, and low-latency inference

  This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.

  ML System DesignOnsite
• CCognitivMedium·Machine Learning Engineer
  Design 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
• CohereMedium·Machine Learning Engineer
  Design How an LLM Detects Questions Beyond Its Training Cutoff and Says So

  Design how a large language model recognizes questions that depend on information after its training cutoff and tells the user where its knowledge ends. The question tests data construction for calibrated abstention, prompt versus fine-tuning versus retrieval trade-offs, and evaluating both over-refusal and hallucination.

  ML System DesignOnsite