Applied Scientist Interview Questions

Showing 1–13 of 13 results

• AmazonHard·Applied Scientist
  Build 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
• AmazonHard·Applied Scientist
  Deep-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
• AmazonHard·Applied Scientist
  Design 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
• AmazonHard·Applied Scientist
  Design 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
• AmazonMedium·Applied Scientist
  Design 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
• AmazonMedium·Applied Scientist
  Design 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
• AmazonHard·Applied Scientist
  Design 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
• AmazonHard·Applied Scientist
  Explain 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
• AmazonMedium·Applied Scientist
  Explain 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
• MicrosoftMedium·Applied Scientist
  Predict Employee Attrition from an HR Dataset and Explain Its Drivers

  A machine learning design exercise: given a tab-separated HR dataset with features such as age, job role, monthly income and overtime, predict which employees will leave and explain which factors drive attrition. It covers feature processing, model choice, class imbalance, evaluation metrics, threshold selection and interpretability.

  ML System DesignOnsite
• WayfairMedium·Applied Scientist
  Rank Homepage Modules for an E-Commerce Product

  Rank e-commerce homepage modules using business objectives, seasonal eligibility, slot effects, slate diversity, cold-start exploration, and page-level experiments.

  ML System DesignOnsite
• WayfairMedium·Applied Scientist
  Set Bids for Search Advertising from Business Value

  Design search-ad bidding from expected business value, with budget pacing, delayed conversion feedback, auction assumptions, and incremental-profit evaluation.

  ML System DesignTechnical Screen
• EeBayEasy·Applied Scientist
  Design fast query-to-listing retrieval over tens of millions of listings

  An ML system design question on retrieving relevant listings for free-text search queries across a catalog of tens of millions of items with low latency. It tests candidate generation, indexing, embedding training, freshness, filtering and scaling, plus the offline and A/B test metrics used to evaluate retrieval.

  ML System DesignTechnical Screen