Machine Learning Interview Questions

Showing 151–180 of 348 results

• LyftHard·Data Scientist
  Develop Dynamic-Pricing Algorithm for Lyft Balancing Key Factors

  Evaluates dynamic-pricing system design for Lyft's two-sided marketplace. Strong answers separate prediction from control, include real-time demand and supply features, model elasticity and ETA response, optimize with fairness and stability constraints, and validate through simulation and experiments.

  Machine LearningTechnical Screen
• LyftMedium·Machine Learning Engineer
  Explain Transformer and Fine-Tuning Basics

  This question evaluates understanding of transformer architectures, embedding models, and transfer learning concepts such as pretraining and fine-tuning, testing competence in representation learning and model adaptation.

  Machine LearningTechnical Screen
• LyftMedium·Machine Learning Engineer
  Implement K-Means Clustering from Scratch in Plain Python or NumPy

  A live coding exercise that asks you to implement k-means clustering from scratch in Python, with NumPy optional, and make it pass the interviewer's test cases. It tests the assignment and update steps, initialization, convergence, empty clusters and writing runnable code under time pressure.

  Machine LearningTechnical Screen
• LyftMedium·Machine Learning Engineer
  Reason About an Intercept Shift in Logistic Regression

  Tests understanding of how an additive shift to the intercept of a logistic regression model impacts predicted probabilities, ranking metrics (ROC-AUC), thresholded classification metrics (Recall, FPR, Precision), and calibration.

  Machine LearningTechnical ScreenLogistic RegressionModel EvaluationClassification Metrics
• LyftMedium·Machine Learning Engineer
  Train vs Test Error Diagnosis, Overfitting Fixes by Model Type, and Cross-Validation

  A machine learning fundamentals screen that asks you to diagnose models from pairs of training and test error, including the unusual case where the test error is the lower one. It continues with curbing overfitting in linear, tree-based and neural network models, and with setting up validation splits and k-fold cross-validation.

  Machine LearningTechnical Screen
• MercorMedium·Machine Learning Engineer
  Evaluate Noisy Data for LLM Post-Training

  Design a curation and validation framework to evaluate, filter, and safely integrate a large, noisy dataset into an LLM post-training pipeline without degrading model performance.

  Machine LearningTechnical ScreenLLM Post-TrainingData CurationModel Evaluation
• MetaMedium·Machine Learning Engineer
  Derive Linear Regression Solution

  This problem tests your ability to derive the Ordinary Least Squares (OLS) closed-form solution for a simple linear regression model and analyze the underlying mathematical assumptions.

  Machine LearningTechnical ScreenLinear RegressionMathematicsMLE
• MetaMedium·Data Scientist
  Employ Collaborative Filtering for Personalized Recommendation Lists

  Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Evaluate Classifier with Precision, Recall, and Fairness Metrics

  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 Evaluate Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Data Scientist
  How to design Shop ad ranking

  This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.

  Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems
• MetaEasy·Data Scientist
  How would you design a Shop Ads ranking algorithm?

  This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.

  Machine LearningTechnical ScreenSystem DesignRankingAds
• MetaMedium·Data Scientist
  Identify Algorithms for Detecting Malicious Duplicated Content

  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 Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MetaHard·Machine Learning Engineer
  Implement 1NN Embeddings and Forward Pass

  Implement a fully vectorized forward pass that projects input features into an embedding space and classifies them using a 1-nearest-neighbor lookup against a reference bank.

  Machine LearningTechnical Screennumpydeep-learninglinear-algebra
• MetaMedium·Machine Learning Engineer
  Implement K-Means Clustering From Scratch

  An ML coding question that asks for a from-scratch implementation of k-means clustering with no provided test cases. It tests the assignment and update steps, vectorized distance computation, initialization, stopping rules, empty-cluster handling and the ability to verify code with self-constructed checks.

  Machine LearningTechnical Screen
• MetaMedium·Machine Learning Engineer
  Implement the Forward Pass of Multi-Head Attention From Scratch

  An ML coding question that asks for the forward pass of multi-head attention from scratch. It tests query, key and value projections, splitting and merging heads, scaled dot-product attention with a stable softmax, causal and padding masks, complexity in sequence length, and self-made correctness checks.

  Machine LearningTechnical Screen
• MetaMedium·Machine Learning Engineer
  Masked Multi-Head Attention Forward Pass Without a Framework

  Implement batched multi-head self-attention by hand, including projections, head split and merge, scaled dot products, a numerically stable masked softmax, and causal and padding masks, then prove it correct with tests you design yourself. Tests shape discipline and attention fundamentals.

  Machine LearningTechnical Screentransformersattentionnumerical-stability
• MetaMedium·Data Scientist
  What features and feature selection would you use?

  This question evaluates feature engineering, feature selection, and production-aware machine learning system design skills for ranking shop ads, within the Machine Learning domain for a Data Scientist role.

  Machine LearningTechnical Screen
• MetaMedium·Data Scientist
  Which clustering algorithm would you use and why

  A Meta Data Scientist machine learning screen on choosing a clustering algorithm for social-product users. It contrasts traditional feature-vector clustering (k-means, GMM, hierarchical, DBSCAN/HDBSCAN) with social-graph community detection (Louvain/Leiden, spectral, SBM, node embeddings), and covers preprocessing, choosing the number of clusters, evaluation, directed/weighted graphs, and scaling to millions of users.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Compute and plot a precision–recall curve

  This question evaluates understanding of binary classification evaluation metrics—precision, recall, precision–recall curves, and related summaries like Average Precision/AUPRC—within the Machine Learning domain and is relevant for Data Scientist roles.

  Machine LearningTechnical Screen
• MicrosoftHard·Data Scientist
  Design a model for imbalanced conversions

  This question evaluates a data scientist's ability to design and validate end-to-end predictive models for imbalanced binary outcomes, encompassing feature engineering, class imbalance handling, probability calibration, thresholding for budgeted targeting, validation, monitoring, and interpretability.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  Explain Deep Learning to a 5-Year-Old Child

  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 Deep Learning to a 5-Year-Old Child states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and PCA and key tradeoffs

  Evaluates understanding of K-Nearest Neighbors (instance-based classification/regression) and Principal Component Analysis (linear dimensionality reduction), highlighting tradeoffs such as distance metric and preprocessing effects, computational and high-dimensional limitations, PCA’s optimization/computation approaches, and the impact of dimensionality reduction on downstream nonparametric methods. Common in the Machine Learning domain for Data Scientist internships at a fundamentals-to-intermediate abstraction level because it probes both theoretical foundations and practical considerations for applying non-parametric algorithms and linear feature extraction to real datasets.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain KNN and how to tune it

  This question evaluates understanding of the K-Nearest Neighbors algorithm and related competencies such as hyperparameter selection (K, distance metric, weighting), data preprocessing impacts, failure modes in high-dimensional or imbalanced settings, and model evaluation strategies.

  Machine LearningTechnical Screen
• MicrosoftEasy·Data Scientist
  Explain SHAP and build an ML project

  This question tests your conceptual understanding of SHAP values for model interpretability and your ability to design a robust, end-to-end machine learning pipeline for a real-world business problem.

  Machine LearningTechnical ScreenSHAPInterpretabilityMLOps
• MicrosoftMedium·Data Scientist
  Explain SHAP in an ML System

  This question evaluates a data scientist's competence in designing and operationalizing end-to-end machine learning systems in the Machine Learning domain, covering problem framing, label and feature design, model selection, evaluation, deployment, monitoring, and model interpretability using SHAP.

  Machine LearningTechnical Screen
• MicrosoftMedium·Machine Learning Engineer
  Explain metrics, regularization, and ablation studies

  This question evaluates understanding of classification evaluation metrics, regularization methods, and experimental design for component-level analysis in NLP and broader Machine Learning systems, testing competencies in performance measurement, regularization trade-offs, and causal attribution of model components.

  Machine LearningTechnical Screen
• MicrosoftMedium·Software Engineer
  Greedy and Top-k Beam Search Decoding That Returns the Sentence Probability

  A coding question on decoding a sequence from a next-token probability distribution: implement greedy decoding, extend it to beam search that keeps the top k partial sequences, and return the chosen sequence with its overall probability. It also probes why long sequences tend to receive low probability and how decoding should account for that.

  Machine LearningTechnical Screen
• MicrosoftMedium·Data Scientist
  How would you build and evaluate a classifier?

  This problem tests your ability to design a binary classification pipeline from metric selection to production monitoring, with a focus on aligning technical decisions with business objectives.

  Machine LearningTechnical ScreenClassificationModel EvaluationImbalanced Data
• MillenniumMedium·Software Engineer
  How would you model stock price prediction?

  This problem tests the candidate's ability to design an end-to-end machine learning pipeline for financial time-series forecasting, specifically focusing on the nuances of feature engineering and evaluation in a non-stationary environment.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringFinancial Engineering
• Mistral AIMedium·Software Engineer
  Compare RMSNorm and LayerNorm in Transformer Blocks

  Compare LayerNorm and RMSNorm equations, centering, shift behavior, hidden-feature axes, numerical precision, and the tradeoffs behind simpler normalization.

  Machine LearningTechnical Screen