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Machine Learning Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 121–150 of 348 results
- GoogleHardData ScientistModel Soccer Shot Conversion
This problem tests the ability to design an end-to-end predictive model for sports events, focusing on feature engineering, handling spatial data sparsity, and evaluating probabilistic outputs.
Machine LearningTechnical ScreenLogistic RegressionModel CalibrationFeature Engineering - GoogleHardData ScientistPredict and act on contract renewal risk
This question evaluates competency in applied machine learning for enterprise churn prediction, testing skills in problem framing, feature engineering, model selection, data hygiene, evaluation metrics, and decisioning within the Machine Learning domain.
Machine LearningTechnical Screen - GsaMediumData ScientistCritique a CNN-Based Bridge Safety Proposal
This question tests the ability to identify flaws in a computer vision project, focusing on data validity, label sparsity, adversarial robustness, and the practical limitations of predictive modeling in structural engineering.
Machine LearningTechnical ScreenComputer VisionAdversarial MLData Ethics - GsaHardData ScientistPredict Stock Returns from Sentiment and Market Data
This problem tests the ability to design an end-to-end predictive pipeline for stock returns, focusing on time-series integrity, feature engineering, and robust evaluation metrics.
Machine LearningTechnical ScreenFinancial ModelingTime SeriesData Leakage - GustoMediumData ScientistBuild Churn Prediction and Survival Models
This problem tests the ability to design an end-to-end churn prediction pipeline, specifically focusing on the distinction between binary classification and survival analysis in the context of customer retention.
Machine LearningTechnical ScreenChurn PredictionSurvival AnalysisFeature Engineering - Hudson River TradingMediumData ScientistDerive expected inversions and mean distribution
This problem tests your understanding of linearity of expectation and variance properties applied to permutation statistics, as well as the ability to design a simulation to verify analytical derivations.
Machine LearningTechnical ScreenProbabilityStatisticsExpectation - Hudson River TradingMediumData ScientistSolve Dice Probability and Matrix Questions
This problem tests probability distributions over modular arithmetic and the identification of properties of doubly stochastic matrices.
Machine LearningTechnical ScreenProbabilityLinear AlgebraMatrices - IBMEasyData ScientistDesign a lead-scoring model
Design an end-to-end lead-scoring model for a marketing/growth data science team: define the label and scoring time, engineer leakage-safe features, compare a logistic-regression baseline with gradient-boosted trees, and evaluate with capacity-aware ranking metrics. The question also probes multicollinearity, model interpretability (SHAP/reason codes), routing-threshold design, and operational risks like selection bias, class imbalance, fairness, and drift.
Machine LearningTechnical Screen - InceptionMediumMachine Learning EngineerImplement Autoregressive Decoding in PyTorch
This question evaluates a candidate's ability to implement autoregressive decoding and sampling strategies in PyTorch, testing competencies in sequence generation, probabilistic sampling methods (greedy, temperature, top-k, top-p), batching, and end-of-sequence handling.
Machine LearningTechnical Screen - IndeedMediumMachine Learning EngineerImplement MSE Loss and Linear Regression from Scratch
Implement the mean squared error loss and a linear regression model with fit and predict methods, written from scratch rather than with a library model. It tests deriving and coding the gradients, choosing a training method, feature scaling, input validation, and verifying the implementation.
Machine LearningTechnical Screen - InstacartHardMachine Learning EngineerExplain Core ML Concepts
This question tests the conceptual understanding of supervised learning foundations, the mathematical relationship between regression techniques, and the convergence behavior of optimization algorithms.
Machine LearningTechnical ScreenLinear RegressionLogistic RegressionOptimization - IntuitMediumData ScientistEngineer and Impute ZIP Features
This Intuit data scientist machine learning screen evaluates feature engineering from address/ZIP data, joining external census-style datasets, high-cardinality encoding, and missing-ZIP imputation. It also tests awareness of fairness, privacy, leakage, and how to validate whether geographic features actually improve a model.
Machine LearningTechnical Screen - IntuitMediumSoftware EngineerExplain LLMs, AI-native vs. conventional apps, daily AI use, and the dangers of AI
Staff-level backend interview discussion on AI fluency. Explain what a large language model is, contrast AI-native products with conventional apps that add AI features, describe how you use AI tools in daily engineering work, and weigh the main dangers of AI with concrete mitigations.
Machine LearningTechnical Screen - IntuitEasySoftware EngineerWhen should products use AI?
This question evaluates product-oriented decision-making about AI adoption, testing competencies in machine learning product strategy, trade-off analysis (accuracy, latency, cost, explainability, safety, and user trust), and the ability to distinguish AI-native architectures from conventional feature additions.
Machine LearningTechnical Screen - J.P. MorganMediumQuantitative ResearcherExplain Daily Signal Construction and a Prediction Workflow
Explain daily quantitative signal construction and prediction at an appropriate level of disclosure, with point-in-time inputs, workflow validation, ownership, and confidentiality boundaries.
Machine LearningTechnical Screen - J.P. MorganMediumData ScientistExplain Overfitting and Transformer Basics
This question tests your conceptual understanding of model generalization, the mechanics of the Transformer architecture, and the trade-offs involved in sequence modeling.
Machine LearningTechnical ScreenDeep LearningTransformersBias-Variance Tradeoff - Jane StreetEasyQuantitative TraderBounds on the Probability of Rain on at Least One Weekend Day
This question evaluates understanding of probability theory and dependence between events, specifically the ability to reason about bounds on the probability of a union given fixed marginals, which is a core competency for probabilistic modeling in Machine Learning and Data Scientist roles.
Machine LearningTechnical Screen - Jane StreetHardMachine Learning EngineerBuild a time-series forecasting model
Build a time-series forecasting model evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - Jane StreetMediumQuantitative ResearcherDesign a Real-vs-Fake DNA Classifier
A Jane Street Data Scientist technical-screen question on building a classifier that distinguishes real from fake DNA sequences, given a small labeled real/fake set plus a large real-only corpus. It tests sequence representation (k-mers, embeddings, CNN/Transformer), hybrid supervised + anomaly-detection framing, realistic hard-negative generation, evaluation under class imbalance, leakage prevention, threshold calibration, and biological failure modes like reverse-complement symmetry and GC-content shortcuts.
Machine LearningTechnical Screen - Jane StreetEasyQuantitative TraderFour Fair Coins: Expected Payoff and Pricing Two Magic Wands
The question evaluates probabilistic reasoning, expected-value computation, and invariant-based state-space analysis as applied to adaptive operations on random variables and pricing contingent instruments.
Machine LearningTechnical Screen - Jane StreetMediumQuantitative TraderPricing a Mystery Money Box: Expected Value, Adverse Selection, and Competition
This question evaluates probabilistic reasoning and economic-incentives competencies, including expected-value calculation, risk-neutral decision-making, adverse selection, and surplus division in bargaining contexts.
Machine LearningTechnical Screen - Jane StreetMediumQuantitative TraderSealed-Bid Auction for a Box of 200 Coin Flips (and an Informed Opponent)
This question evaluates understanding of probability and expected-value reasoning, auction and game-theoretic strategic thinking, Bayesian inference under asymmetric information, and quantitative decision-making relevant to a Machine Learning-focused data scientist role.
Machine LearningTechnical Screen - Jump TradingHardSoftware EngineerAccelerate Tree-Model Inference
Design an end-to-end strategy to accelerate inference latency and throughput for an existing gradient-boosted decision tree (GBDT) model in a low-latency trading environment while preserving exact prediction fidelity. Address feature engineering constraints, low-level execution optimizations, verification of numerical equivalence, and production drift monitoring.
Machine LearningTechnical ScreenTree ModelsInference OptimizationLow-Latency Systems - LinkedInEasyData ScientistDesign a short-video recommender system
This question assesses the ability to architect a multi-stage machine learning pipeline for a short-video feed, covering candidate generation, ranking, and performance evaluation.
Machine LearningTechnical ScreenSystem DesignRecommendationRanking - LinkedInMediumMachine Learning EngineerExplain Core ML Fundamentals
This question assesses core conceptual knowledge of supervised learning, optimization strategies, and regularization techniques used in model training.
Machine LearningTechnical ScreenOptimizationRegularizationDecision Trees - LinkedInMediumMachine Learning EngineerExplain gradient descent, learning rate, backpropagation, overfitting and batch size
A machine learning fundamentals screen on how neural networks are trained: how gradient descent updates parameters, what the learning rate and batch size change, how backpropagation computes gradients, and how to diagnose and fix underfitting and overfitting. Tests mechanics and practical training judgment, not definitions alone.
Machine LearningTechnical Screen - LinkedInEasyMachine Learning EngineerExplain overfitting vs underfitting and fixes
This question tests the conceptual understanding of the bias-variance tradeoff and the practical strategies used to improve model generalization.
Machine LearningTechnical ScreenModel EvaluationBias-Variance TradeoffRegularization - LinkedInMediumData ScientistExplain variance reduction in random forests
This question tests your conceptual understanding of how Random Forests reduce the variance of base decision trees through bagging and feature randomness.
Machine LearningTechnical ScreenEnsemble MethodsRandom ForestBias-Variance Trade-off - LinkedInEasyData ScientistHandle imbalance, validate samples, and avoid overfitting
This question tests your ability to design a robust machine learning pipeline for a highly imbalanced classification task, focusing on sampling strategies, metric selection, and regularization techniques to prevent overfitting.
Machine LearningTechnical ScreenImbalanceOverfittingRegularization - LinkedInHardMachine Learning EngineerImplement a Text-Embedding Recommender Training Pipeline
Tests the ability to structure an end-to-end, leakage-resistant training pipeline for a text-embedding recommender, covering profile flattening, embedding caching, temporal splits, binary classification, and artifact serialization.
Machine LearningTechnical Screenrecommender-systemsfeature-engineeringnlp-embeddings
Google
Gusto
Hudson River Trading
IBM
Indeed
Instacart
Intuit
J.P. Morgan
Jane Street
LinkedIn