Machine Learning interview questions

Showing 151–180 of 572 results

• Choose Between Precision and Recall for a Classification DecisionEasy

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Classify Variable-Length, Unordered Point Sequences with Missing FeaturesHard

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Decide Whether an LSTM Is Appropriate for Sequence DataEasy

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Design Framework for Robust House-Price Prediction ModelHard

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Design city home-price prediction systemHard

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Determine When a Quadratic Has Finite MinimumMedium

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Diagnose outliers and influence in linear regressionHard

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Estimate OLS via streaming sufficient statisticsHard

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Explain factor leakage checks and IC/ICIR filteringMedium

  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.

  CitadelData ScientistMachine LearningTechnical Screen
• Make a hard MoE router differentiableHard

  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.

  CitadelMachine Learning EngineerMachine LearningTechnical Screen
• Defend Your Own Research Paper: Experimental Setup, Practicality and Later WorkMedium

  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.

  CohereMachine Learning EngineerMachine LearningOnsite
• Explain LLM training, RL, and evaluationMedium

  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.

  CohereMachine Learning EngineerMachine LearningOnsite
• Explain Top-k and Top-p DecodingMedium

  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.

  CohereMachine Learning EngineerMachine LearningOnsite
• Build a baseline classification model from messy dataMedium

  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.

  CoinbaseMachine Learning EngineerMachine LearningOnsite
• Explain precision/recall and compute NN outputHard

  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.

  CoinbaseMachine Learning EngineerMachine LearningOnline Assessment
• Predicting the Next Elevator Call LocationMedium

  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.

  CoupangData ScientistMachine LearningTechnical Screen
• Design sequence decoding with greedy and beam searchMedium

  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.

  CrestaSoftware EngineerMachine LearningTechnical Screen
• Explain core ML and DL fundamentalsMedium

  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.

  DRWMachine Learning EngineerMachine LearningOnline Assessment
• Why do transformers struggle with long context?Medium

  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.

  DandyMachine Learning EngineerMachine LearningHR Screen
• Explain Linear Regression Feature Transformation EquivalenceMedium

  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.

  DatabricksData ScientistMachine LearningOnline Assessment
• Implement Gradient Descent RegressionMedium

  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.

  DatabricksMachine Learning EngineerMachine LearningOnsite
• Implement Linear Regression Gradient DescentMedium

  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.

  DatabricksMachine Learning EngineerMachine LearningOnsite
• Implement Simple Linear Regression with Gradient Descent from ScratchMedium

  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.

  DatabricksMachine Learning EngineerMachine LearningOnsite
• Implement Focal Loss for Class-Imbalanced ClassificationHard

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

  DatadogMachine Learning EngineerMachine LearningTechnical Screen
• Implement Grouped-Query Attention (GQA)Hard

  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.

  DatadogMachine Learning EngineerMachine LearningTechnical Screen
• Explain and Compare SGD and AdamHard

  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.

  DeshawSoftware EngineerMachine LearningTechnical Screen
• Build Naive Bayes spam classifier with F1Medium

  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.

  DisneyMachine Learning EngineerMachine LearningTechnical Screen
• Build ETA prediction and simulate impactHard

  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.

  DoorDashData ScientistMachine LearningOnsite
• Build a late-delivery risk modelHard

  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.

  DoorDashData ScientistMachine LearningOnsite
• Design a Homepage Store RecommenderHard

  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.

  DoorDashData ScientistMachine LearningOnsite