Machine Learning Engineer Interview Questions

Showing 1–30 of 67 results

• 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
• 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 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
• 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
• 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
• 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
• 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
• CCreditkarmaMedium·Machine Learning Engineer
  Design 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
• DoorDashHard·Machine Learning Engineer
  Discuss 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
• Freddie MacMedium·Machine Learning Engineer
  Improve 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
• GoogleMedium·Machine Learning Engineer
  Design 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
• GoogleMedium·Machine Learning Engineer
  Design 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
• GoogleHard·Machine Learning Engineer
  Design 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
• GoogleHard·Machine Learning Engineer
  Design 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
• GoogleHard·Machine Learning Engineer
  Explain 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
• Jane StreetHard·Machine Learning Engineer
  Design 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 StreetHard·Machine Learning Engineer
  Predict 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
• LinkedInHard·Machine Learning Engineer
  Design a Model That Predicts Whether a Member Clicks a Displayed Job

  An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.

  ML System DesignTechnical Screen
• LinkedInMedium·Machine Learning Engineer
  Design a Personalized Feed and Compare Ranking Objectives

  Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.

  ML System DesignTechnical Screenml-system-designrecommendation-systemslearning-to-rank
• LinkedInMedium·Machine Learning Engineer
  Design an ML Forecasting and Pacing System for Ad Bidding

  Design an ad forecasting and budget pacing architecture that combines predictive ML with deterministic budget guardrails to optimize campaign delivery across non-stationary traffic.

  ML System DesignTechnical Screenml-system-designad-techtime-series
• MercorMedium·Machine Learning Engineer
  Build an Ollama Eval Harness

  This question evaluates a candidate's ability to design and implement a lightweight evaluation harness for a locally hosted language model, testing competencies in ML system design, service integration, metrics instrumentation, reliability engineering (timeouts, retries), concurrency control, configuration management, and extensibility.

  ML System DesignTechnical Screen
• MetaMedium·Machine Learning Engineer
  Design a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and Rewards

  An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.

  ML System DesignTechnical Screen
• MetaMedium·Machine Learning Engineer
  Design a Short-Video Recommendation System

  This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.

  ML System DesignTechnical ScreenRecommendation SystemsScalabilityRanking
• MetaHard·Machine Learning Engineer
  Design image and multimodal generation systems

  This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.

  ML System DesignTechnical Screen
• MetaMedium·Machine Learning Engineer
  Prevent Private Code Leakage in Coding Agents

  This problem tests the ability to design an ML-driven safeguard system that prevents private codebase exposure during the training, fine-tuning, and inference phases of a coding agent.

  ML System DesignTechnical ScreenMLOpsSecurityPrivacy