Machine Learning Interview Questions

Showing 151–180 of 572 results

• CitadelEasy·Data Scientist
  Choose Between Precision and Recall for a Classification Decision

  Review precision and recall for imbalanced classification and learn how both change with the decision threshold. Choose an operating point from error costs, capacity, calibration, uncertainty, and segment performance instead of treating either metric as universally best.

  Machine LearningTechnical Screen
• CitadelHard·Data Scientist
  Classify Variable-Length, Unordered Point Sequences with Missing Features

  A staged live-coding ML task: classify whole sequences of 6-feature points as 0 or 1, then adapt the model as lengths vary from 16 to 100, point order stops being meaningful, and some features go missing. Tests model selection, length and permutation invariance, and practical missing-value handling.

  Machine LearningTechnical Screen
• CitadelEasy·Data Scientist
  Decide Whether an LSTM Is Appropriate for Sequence Data

  Decide whether an LSTM is appropriate for sequential data by defining the sequence unit, prediction horizon, masking, and time-safe validation. Compare the model with lag-based and simpler baselines while considering data size, overfitting, latency, and maintainability.

  Machine LearningTechnical Screen
• CitadelHard·Data Scientist
  Design Framework for Robust House-Price Prediction Model

  Evaluates robust house-price prediction modeling across diagnostics, features, scale, and validation. Strong answers cover linear regression diagnostics, Cook's distance, leverage, Random Forest complexity controls and variable importance, housing-market feature engineering, leakage prevention, large-scale training, and time-geography validation.

  Machine LearningTechnical Screen
• CitadelHard·Data Scientist
  Design city home-price prediction system

  This question evaluates proficiency in end-to-end machine learning system design for geospatial property price prediction, covering skills such as feature engineering, time- and spatial-aware validation, model selection, interpretability, fairness/compliance, deployment, monitoring, and error analysis.

  Machine LearningTechnical Screen
• CitadelMedium·Data Scientist
  Determine When a Quadratic Has Finite Minimum

  This question evaluates understanding of quadratic optimization, linear algebra, and convexity concepts—particularly properties of quadratic forms, matrix symmetry, and conditions for boundedness in real-valued unconstrained minimization—and why these properties matter for existence of minima.

  Machine LearningTechnical Screen
• CitadelHard·Data Scientist
  Diagnose outliers and influence in linear regression

  This question evaluates a data scientist's competency in diagnosing outliers, high-leverage points, influential observations, and Cook's distance in ordinary least squares regression, including recognition of robust alternatives and rationale for reporting model-review decisions.

  Machine LearningTechnical Screen
• CitadelHard·Data Scientist
  Estimate OLS via streaming sufficient statistics

  This question evaluates proficiency in streaming/out-of-core linear regression, including computing sufficient statistics with an intercept, assessing numerical stability of normal equations versus QR/SVD or incremental methods, incorporating ridge penalties, and designing parallel fault-tolerant computations.

  Machine LearningTechnical Screen
• CitadelMedium·Data Scientist
  Explain factor leakage checks and IC/ICIR filtering

  This question tests the conceptual understanding of signal quality metrics and the practical steps required to prevent look-ahead bias and overfitting when developing quantitative trading factors.

  Machine LearningTechnical ScreenQuantitative FinanceAlpha ModelingBacktesting
• CitadelHard·Machine Learning Engineer
  Make a hard MoE router differentiable

  Make a hard MoE router differentiable 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
• CohereMedium·Machine Learning Engineer
  Defend Your Own Research Paper: Experimental Setup, Practicality and Later Work

  A research paper round where you present your own published paper to an interviewer who already knows it and questions its weaknesses. Prepare to judge whether the experimental setup was reasonable, whether the results are practical, and what the field has learned since publication, all with specific, honest self-critique.

  Machine LearningOnsite
• CohereMedium·Machine Learning Engineer
  Explain LLM training, RL, and evaluation

  This question tests your conceptual understanding of the end-to-end LLM lifecycle, specifically the transition from raw text pre-training to alignment and evaluation.

  Machine LearningOnsiteLLMRLHFPPO
• CohereMedium·Machine Learning Engineer
  Explain Top-k and Top-p Decoding

  This question tests your conceptual and mathematical understanding of Top-k and Top-p (nucleus) sampling, focusing on how they modify the output probability distribution of autoregressive language models to balance creativity and coherence.

  Machine LearningOnsiteNLPGenerative AISampling
• CoinbaseMedium·Machine Learning Engineer
  Build a baseline classification model from messy data

  This question evaluates skills in practical machine learning engineering, including data cleaning, preprocessing, feature selection, handling mixed numeric and categorical features, and baseline model construction for a binary classification task.

  Machine LearningOnsite
• CoinbaseHard·Machine Learning Engineer
  Explain precision/recall and compute NN output

  This question tests the ability to calculate classification performance metrics, explain ensemble model dynamics, and perform manual forward propagation through a small neural network architecture.

  Machine LearningOnline AssessmentClassificationNeural NetworksEnsemble Methods
• CoupangMedium·Data Scientist
  Predicting the Next Elevator Call Location

  This question tests the ability to design a spatio-temporal forecasting system to predict future elevator call locations, focusing on feature engineering, model selection, and handling data sparsity.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringSpatio-Temporal
• CrestaMedium·Software Engineer
  Design sequence decoding with greedy and beam search

  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 sequence decoding with greedy and beam search states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• DRWMedium·Machine Learning Engineer
  Explain core ML and DL fundamentals

  Explain core ML and DL fundamentals 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 LearningOnline Assessment
• 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
• DatabricksMedium·Data Scientist
  Explain Linear Regression Feature Transformation Equivalence

  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 Explain Linear Regression Feature Transformation Equivalence states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnline Assessment
• DatabricksMedium·Machine Learning Engineer
  Implement Gradient Descent Regression

  This problem tests your understanding of linear regression by requiring the implementation of a gradient descent optimizer from scratch to minimize the Mean Squared Error loss.

  Machine LearningOnsiteLinear RegressionGradient DescentOptimization
• DatabricksMedium·Machine Learning Engineer
  Implement Linear Regression Gradient Descent

  This question tests the ability to implement batch gradient descent for simple linear regression from scratch, requiring the derivation of gradients for weights and bias, and understanding how to optimize convergence.

  Machine LearningOnsiteGradient DescentLinear RegressionOptimization
• DatabricksMedium·Machine Learning Engineer
  Implement Simple Linear Regression with Gradient Descent from Scratch

  Write simple linear regression trained with gradient descent from scratch, choosing the loss, learning rate and stopping rule yourself. Tests deriving the gradients correctly, writing a runnable training loop, understanding feature scaling and convergence, and verifying results against a closed-form fit.

  Machine LearningOnsite
• DatadogHard·Machine Learning Engineer
  Implement Focal Loss for Class-Imbalanced Classification

  Tests the ability to implement binary focal loss with raw logits from first principles, emphasizing numerical stability, custom weighting, and tensor reduction strategies.

  Machine LearningTechnical Screenmachine-learningdeep-learningpytorch
• DatadogHard·Machine Learning Engineer
  Implement Grouped-Query Attention (GQA)

  Implement the Grouped-Query Attention (GQA) mechanism commonly used in modern LLMs to balance serving memory bandwidth with generation quality. You will handle tensor reshaping/expansion across query head groups, scaled dot-product attention, and causal masking.

  Machine LearningTechnical Screendeep-learningtransformerspytorch
• DDeshawHard·Software Engineer
  Explain and Compare SGD and Adam

  Explain how stochastic gradient descent updates model parameters and how Adam modifies those updates using moving estimates of first and second gradient moments. Cover data and labels, leakage-safe features, baselines and model choice, offline evaluation, deployment constraints, monitoring, and drift.

  Machine LearningTechnical Screen
• DisneyMedium·Machine Learning Engineer
  Build Naive Bayes spam classifier with F1

  This question evaluates understanding of probabilistic text classification with Naive Bayes, text preprocessing and feature extraction, and use of the F1 score for performance measurement in a binary spam detection task.

  Machine LearningTechnical Screen
• DoorDashHard·Data Scientist
  Build ETA prediction and simulate impact

  This question evaluates machine learning competencies for ETA prediction, including feature engineering, leakage detection, time-based validation, point and quantile modeling, evaluation and calibration metrics, cost-sensitive decisioning, explainability and fairness auditing, and production deployment considerations.

  Machine LearningOnsite
• DoorDashHard·Data Scientist
  Build a late-delivery risk model

  This question evaluates competencies in production-ready predictive modeling, including target definition, feature engineering, temporal train/validation splitting to avoid leakage, model evaluation and calibration, monitoring and ramp strategies, and translating probabilistic outputs into business-impacting decisions for a Data Scientist.

  Machine LearningOnsite
• 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