Machine Learning Interview Questions

Showing 61–90 of 163 results

• DoorDashHard·Data Scientist
  Design a Homepage Store Recommender

  This question assesses the ability to design an end-to-end, low-latency recommendation system for a food delivery marketplace, focusing on candidate retrieval strategies, real-time personalization, and system architecture.

  Machine LearningOnsiteRecommender SystemsSystem DesignRanking
• DoorDashHard·Data Scientist
  Design a Low-Latency Store Recommender

  This question assesses the ability to architect a multi-stage recommendation pipeline that balances low-latency retrieval with high-accuracy ranking in a geo-aware, high-concurrency food delivery context.

  Machine LearningOnsiteSystem DesignRecommendation SystemsLatency
• DoorDashHard·Data Scientist
  Design and evaluate an uplift model

  This question evaluates competency in causal inference and uplift modeling, experimental design for randomized treatment allocation, offline evaluation of uplift models, operational decision rules under budget constraints, fairness guardrails, and handling interference and deployment of sequential tests in a data-science context.

  Machine LearningOnsite
• ExpediaHard·Data Scientist
  Validate and monitor ranking model end-to-end

  This question evaluates a data scientist's mastery of learning-to-rank concepts, offline evaluation and metric design, position-bias correction, diagnostic analysis, deployment safeguards, and alignment of surrogate objectives with business KPIs for hotel search ranking.

  Machine LearningOnsite
• GoogleMedium·Data Scientist
  Address Overfitting in Supervised Learning Models

  Evaluates supervised learning judgment around bias, variance, overfitting, and train-test performance gaps. Strong answers diagnose leakage, split errors, distribution shift, and high variance, then propose regularization, simpler models, validation, more data, and monitoring.

  Machine LearningOnsite
• GoogleMedium·Data Scientist
  Compare Logistic Regression and Random Forest in Limited Data Scenarios

  Evaluates binary classification model selection with limited labeled data. Strong answers explain logistic regression and cross-entropy loss, compare it with Random Forests, cover L1 and L2 regularization, diagnose overfitting, and contrast Random Forests with boosting.

  Machine LearningOnsite
• GoogleMedium·Machine Learning Engineer
  Compare NLP tokenization and LLM recommendations

  This question tests the conceptual understanding of tokenization strategies in NLP and the architectural trade-offs when integrating LLMs into recommendation systems.

  Machine LearningOnsiteNLPLLMRecommendation-Systems
• GoogleMedium·Data Scientist
  Decide between two vendors under constraints

  This question evaluates a candidate's competency in decision modeling and cost‑sensitive machine learning, including causal inference for selection bias, propensity estimation, constrained optimization for capacity and SLA trade‑offs, and offline/online evaluation in the Machine Learning domain for a Data Scientist role.

  Machine LearningOnsite
• GoogleHard·Data Scientist
  Explain a favorite model end-to-end

  This question evaluates a candidate's end-to-end machine learning competencies, including problem framing, objective and loss selection, inductive biases, feature engineering, validation strategies, training and regularization practices, production issues, monitoring, and fairness/privacy considerations.

  Machine LearningOnsite
• GustoMedium·Data Scientist
  Build a Churn Prediction Model

  This question tests the ability to design an end-to-end churn prediction pipeline, focusing on temporal data integrity, class imbalance, and actionable evaluation metrics for business stakeholders.

  Machine LearningOnsiteChurnFeature EngineeringImbalanced Data
• HarveyMedium·Software Engineer
  Embedding Retrieval with Cosine Similarity in a Notebook, and Choosing the Metric

  Implement embedding-based document retrieval with cosine similarity in a Python notebook, then explain how cosine similarity relates to dot product and Euclidean distance and how to measure retrieval quality. It tests vectorized top-k search, deterministic ties, index metric choice and evaluation metrics.

  Machine LearningOnsite
• HarveyMedium·Machine Learning Engineer
  Training LLM Agents for Long-Context, Long-Horizon Tasks: Environments and Rewards

  An ML research discussion on training language-model agents for long-context, long-horizon tasks. It tests the training recipe from context extension through supervised trajectories to RL, the design of reproducible training environments, and reward design that resists reward hacking over long trajectories.

  Machine LearningOnsite
• Hudson River TradingHard·Software Engineer
  Build a Heart Disease Baseline

  This question evaluates proficiency in applied machine learning workflows, including data inspection and cleaning, exploratory data analysis, feature engineering, baseline binary classification modeling, and evaluation using Python libraries like pandas and seaborn.

  Machine LearningOnsite
• ImcMedium·Data Scientist
  Compute gambler’s ruin probabilities and hitting times

  This question tests your ability to model a discrete random walk as a Markov chain, derive the probability of reaching an absorption state, and calculate the expected time to reach that state.

  Machine LearningOnsiteProbabilityMarkov ChainsStochastic Processes
• ImcMedium·Data Scientist
  Explain linear regression and Transformer fundamentals

  This question tests your ability to explain the theoretical foundations of linear regression, including its probabilistic interpretation, and your technical depth regarding the mechanics of the Transformer architecture.

  Machine LearningOnsiteLinear RegressionTransformersSelf-Attention
• ImcMedium·Data Scientist
  Run EDA and train models while preventing overfitting

  This problem tests your end-to-end machine learning pipeline skills, focusing on tabular regression, feature engineering, validation strategies, and regularization techniques.

  Machine LearningOnsiteRegressionOverfittingEDA
• ImcMedium·Data Scientist
  Solve a constrained problem using KKT conditions

  This question tests the ability to solve a convex optimization problem under inequality constraints using KKT conditions and the identification of active versus inactive constraints.

  Machine LearningOnsiteOptimizationKKTCalculus
• InstacartHard·Data Scientist
  Improve low R² without p‑hacking

  This question evaluates competence in statistical modeling and causal inference, covering regression diagnostics, feature engineering and interactions, appropriate error distributions and link functions, leakage detection, model selection and validation, and the trade-off between predictive accuracy and valid effect estimation.

  Machine LearningOnsite
• IntuitEasy·Data Scientist
  Build a predictive model from TurboTax sample data

  This problem tests your ability to design an end-to-end predictive modeling pipeline for tax-related data, focusing on feature engineering, handling imbalanced datasets, and selecting appropriate evaluation metrics.

  Machine LearningOnsiteClassificationFeature EngineeringImbalanced Data
• IntuitEasy·Product Analyst
  Handle missing and unavailable predictive features

  This question evaluates a product analyst's machine learning competencies in diagnosing data quality issues, handling feature availability and training-serving skew, and interpreting counterintuitive correlations, testing both conceptual understanding and practical application for model development and deployment.

  Machine LearningOnsite
• J.P. MorganMedium·Data Scientist
  Test whether samples follow a binomial distribution

  This problem tests the application of goodness-of-fit testing to verify if a set of discrete observations conforms to a binomial distribution.

  Machine LearningOnsiteStatisticsHypothesis TestingProbability
• Jane StreetMedium·Quantitative Trader
  Dice Duel: 30-Sided Die vs. 20-Sided Die with One Blind Reroll

  This question evaluates probabilistic reasoning, expected-value comparison, and decision-making under uncertainty in discrete random processes. It is commonly asked to test a candidate's ability to reason about optimal stochastic decisions and trade-offs in blind reroll scenarios, classified under probability and decision theory within the Machine Learning domain and assessing conceptual understanding rather than practical implementation.

  Machine LearningOnsite
• Jane StreetMedium·Quantitative Researcher
  Minimum Common Pairwise Correlation Among Seven Identically Distributed Random Variables

  This question evaluates understanding of correlation constraints, covariance/correlation matrix structure, and spectral analysis, combining probability, statistics, and linear algebra competencies within the Machine Learning/Data Science domain.

  Machine LearningOnsite
• LinkedInMedium·Machine Learning Engineer
  Answer practical ML foundations questions

  This question evaluates an interviewee's practical mastery of ML foundations, covering probability calibration and its evaluation, feature selection methods including neural-network approaches, tokenizer types and trade-offs in NLP, and optimizer behavior such as Adam's maintained statistics.

  Machine LearningOnsite
• MercorMedium·Machine Learning Engineer
  Review a Compact GPT-Style Transformer Implementation

  Assess and debug a compact decoder-only Transformer implementation. Candidates trace tensor dimensions through causal multi-head self-attention, identify flaws in next-token loss alignment and KV-cached generation, and establish equivalence between cached and uncached forward passes.

  Machine LearningOnsiteTransformersPyTorchDeep Learning
• MetaMedium·Data Scientist
  Apply reinforcement learning to product decisions

  This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.

  Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation
• MetaMedium·Machine Learning Engineer
  Build harmful-content text classifier

  This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.

  Machine LearningOnsiteNLPClassificationMLOps
• MetaMedium·Data Scientist
  Choose threshold under asymmetric costs

  This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Deploy multi-armed bandits safely

  This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.

  Machine LearningOnsite
• MetaHard·Data Scientist
  Design Machine Learning Model for Facebook Groups Post Ranking

  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 Design Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite