Machine Learning Interview Questions

Showing 1–15 of 15 results

• AAdyenEasy·Machine Learning Engineer
  Build a Payment Fraud Detection Model

  Tests practical machine learning design for payment fraud detection, covering data preprocessing, temporal validation splits, class imbalance handling, and metric selection.

  Machine LearningHR ScreenFraud DetectionClassificationFeature Engineering
• AnthropicMedium·Machine Learning Engineer
  Design a Double Descent Experiment

  Tests your ability to design a controlled synthetic experiment demonstrating double descent, explain the mathematical causes of test error divergence near the interpolation threshold, and analyze the interplay between noise, conditioning, and regularization.

  Machine LearningHR Screendouble-descentgeneralizationlinear-models
• AnthropicMedium·Software Engineer
  Identify the Most Important AI Safety Risk

  Tests your ability to analyze AI alignment failure modes—specifically sycophancy and emotional over-reliance—and formulate rigorous evaluation metrics, training mitigations, and product-level guardrails.

  Machine LearningHR ScreenAI SafetyRLHFSycophancy
• ACAxq CapitalMedium·Software Engineer
  Explain What a Neural Network Is and How It Learns

  Explain what a neural network is: how units and layers turn weighted inputs into outputs, why non-linear activations are essential, and how the weights are learned with a loss function, backpropagation, and gradient descent. It tests core machine learning fundamentals, including overfitting and when simpler models are the better choice.

  Machine LearningHR Screen
• Capital OneMedium·Data Scientist
  Explain MSE vs MAE, AUC, and imbalance handling

  This question evaluates a candidate's understanding of regression loss trade-offs (MAE vs MSE), classification evaluation metrics under severe class imbalance (ROC-AUC vs PR-AUC), neural network output activation and loss dynamics, and practical techniques for imbalance mitigation and probability-threshold selection in binary classification.

  Machine LearningHR Screen
• Capital OneHard·Data Scientist
  Present and defend your data challenge end-to-end

  This question evaluates end-to-end data science and machine learning competency, focusing on problem definition, data provenance and schema, exploratory data analysis, baselines and modeling choices, validation and metrics, error analysis, fairness and robustness, productionization, and reproducibility.

  Machine LearningHR Screen
• DDandyMedium·Machine Learning Engineer
  Why do transformers struggle with long context?

  This question evaluates understanding of transformer architectures, attention mechanisms, and scalability trade-offs, assessing competencies in computational complexity, memory usage, and system-level performance implications.

  Machine LearningHR Screen
• MetaHard·Data Scientist
  Choose and compute recommender evaluation metrics

  This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.

  Machine LearningHR Screen
• NVIDIAHard·Data Scientist
  Diagnose overfitting, DenseNet, preprocessing, CV

  This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.

  Machine LearningHR Screen
• OpenAIMedium·Software Engineer
  Design Restart Strategy for Oracle Solver

  This question tests the ability to optimize restart intervals for a randomized algorithm with heavy-tailed runtime distributions, balancing the trade-off between progress and computation waste.

  Machine LearningHR ScreenProbabilistic ModelingAlgorithmsOptimization
• TikTokHard·Data Scientist
  Design recommendations objective balancing growth and monetization

  This question evaluates a data scientist's competency in designing multi-objective recommender systems that balance user growth and creator monetization, covering ranking objective formulation, per-impression value estimation, constraint enforcement (latency, diversity, fairness/exposure), offline-to-online metric validation, cold-start and exploration strategies, and interference/attribution challenges within the Machine Learning domain. It is commonly asked to assess the ability to reason about trade-offs between business and user metrics, to define measurable objectives and validation plans, and to demonstrate both conceptual understanding and practical application of system design and evaluation.

  Machine LearningHR Screen
• UiPathMedium·Machine Learning Engineer
  Reduce overfitting and explain the mean squared error loss

  A machine learning phone screen on how to diagnose and reduce overfitting, and on the mean squared error loss: its definition, what minimizing it estimates, its probabilistic interpretation, and when another loss works better. Tests fundamentals, mechanisms and trade-off reasoning.

  Machine LearningHR Screen
• UpstartEasy·Data Scientist
  Derive logistic regression objective and gradients

  This question evaluates understanding of logistic regression, maximum likelihood estimation, cross-entropy (negative log-likelihood), L2 regularization, and the ability to compute model gradients for binary classification.

  Machine LearningHR Screen
• VoleonMedium·Data Scientist
  Describe Your Machine Learning Project Experience

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Describe Your Machine Learning Project Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningHR Screen
• xAIMedium·Software Engineer
  How Would You Prevent Hallucinations in an LLM-Based System?

  This question evaluates a candidate's understanding of hallucination in large language models, including underlying causes, detection, and system-level mitigation approaches, as well as their ability to reason about model reliability and failure modes.

  Machine LearningHR Screen