ML System Design Interview Questions

Showing 1–6 of 6 results

• 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
• CitadelHard·Data Scientist
  Build a regression model for wind power output

  This question evaluates competency in feature engineering, physics-informed regression modeling, sensor-data quality handling, model selection and validation, and uncertainty estimation for turbine-level instantaneous power prediction.

  ML System DesignOnline Assessment
• CitadelHard·Software Engineer
  Build models for housing and wind power prediction

  This question evaluates ML system design and applied modeling competencies, including data assumptions, preprocessing, feature engineering, model selection, validation strategies, temporal leakage handling, and end-to-end pipeline construction for a binary housing-affordability classifier and a time-series regression for wind-farm power prediction.

  ML System DesignOnline Assessment
• CitadelHard·Data Scientist
  Design a time-series home-buy decision classifier

  This question evaluates competency in ML system design for time-series decisioning, including rigorous target and horizon definition, temporal feature engineering, time-aware validation, probabilistic calibration and thresholding, leakage controls, model selection trade-offs, and deployment monitoring.

  ML System DesignOnline Assessment
• DRWMedium·Machine Learning Engineer
  Build pipeline for imbalanced classification

  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 Build pipeline for imbalanced classification states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  ML System DesignOnline Assessment
• DRWHard·Machine Learning Engineer
  Train LinearSVC to beat a hidden baseline

  Train LinearSVC to beat a hidden baseline 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 DesignOnline Assessment