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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 163 results
- OpenAIMediumData ScientistImplement NumPy neural-network layers
This question tests your ability to implement fundamental neural network components using NumPy, focusing on matrix operations, broadcasting, and numerical stability.
Machine LearningOnsiteNumPyNeural NetworksDeep Learning - OpenAIMediumMachine Learning EngineerImplement Prefix Products and Their Backward Pass
Work through inclusive prefix products in separate-output, in-place, reverse-gradient, and parallel variants. Explain zeros, overflow, mutation and aliasing, constant temporary workspace, synchronization, floating-point effects, and the work/depth trade-offs of each part.
Machine LearningOnsite - OpenAIHardMachine Learning EngineerImprove Training With Noisy Annotators
This question evaluates a candidate's ability to handle label noise by estimating annotator reliability and applying data-cleaning, relabeling, or reweighting methods while measuring model performance and understanding classification metrics.
Machine LearningOnsite - OpenAIHardMachine Learning EngineerRepair a Broken Tiny Language Model, Then Make Decoding Incremental
Find four planted bugs in a compact PyTorch decoder-only language model (some produce NaNs, some silently ruin learning), then add key/value caching so generation processes one new token per step.
Machine LearningOnsitepytorchtransformersdebugging - OtherHardData ScientistPredict job changes month by month
This question evaluates competency in discrete-time survival modeling and end-to-end predictive system design, encompassing feature engineering, label/hazard formulation, leakage prevention, model evaluation, fairness assessment, and experimentation for production machine learning.
Machine LearningOnsite - PayPalHardData ScientistExplain unsupervised fraud and evaluation
Explain unsupervised fraud and evaluation 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 LearningOnsite - PayPalMediumData ScientistHow to validate production models?
This question assesses the end-to-end strategy for validating a production-ready machine learning model, focusing on rigorous testing frameworks, drift detection, and business-risk mitigation.
Machine LearningOnsiteModel ValidationProduction MLFraud Detection - PinterestMediumData ScientistOptimize Hyper-parameter Search to Prevent Combinatorial Explosion
Evaluates Python grid-search generation and hyperparameter optimization strategy. Strong answers yield lazy Cartesian-product combinations and discuss random search, Bayesian optimization, pruning, early stopping, and budget limits.
Machine LearningOnsite - PinterestHardData ScientistVerify Machine-Learning Fundamentals for E-commerce Recommendation Platform
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 Verify Machine-Learning Fundamentals for E-commerce Recommendation Platform states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - Point72MediumData ScientistExplain How L1 and L2 Regularization Change Linear-Model Coefficients
This question tests the conceptual understanding of how L1 and L2 penalties influence the optimization objective and the resulting structure of the weight vector in a linear regression model.
Machine LearningOnsiteRegularizationLinear RegressionOptimization - RedditMediumMachine Learning EngineerAnalyze CTR Data and Train Model
This problem tests the end-to-end workflow of building a CTR prediction model, focusing on feature engineering for high-cardinality categorical data and handling temporal leakage.
Machine LearningOnsiteFeature EngineeringCTR PredictionData Leakage - RedditMediumMachine Learning EngineerModel y from x and interpret distributions
This question evaluates modeling judgment in machine learning, covering problem framing (regression versus classification), baseline model and metric selection, interpretation of feature or class distribution differences, and cold-start strategies for users, items, and regions.
Machine LearningOnsite - SalesforceMediumMachine Learning EngineerExplain ML and LLM fundamentals
This question evaluates understanding of core machine learning and LLM competencies including evaluation metrics (F1 vs accuracy), differences between classification and regression, fair benchmarking, inference pipeline bottlenecks and optimization, productionization of AI services, and modern LLM concepts such as the Transformer architecture, context engineering, retrieval-augmented generation, grounding, and guardrails. It is commonly asked in Machine Learning interviews to assess both conceptual understanding and practical application, probing the candidate's ability to reason about evaluation trade-offs, system-level performance constraints, and deployment considerations rather than only algorithmic detail.
Machine LearningOnsite - SamsaraMediumMachine Learning EngineerExplain LLM training and evaluation
This question evaluates an engineer's competency in LLM engineering, including transformer pretraining and finetuning, alignment approaches like instruction tuning and RLHF, strategies for reducing hallucinations, evaluation and production monitoring practices, and inference optimizations.
Machine LearningOnsite - Scale AIEasyMachine Learning EngineerExplain LLM post-training methods and tradeoffs
This question assesses the candidate's understanding of the LLM post-training pipeline, specifically how to transition a foundation model into a helpful, safe assistant through various alignment techniques.
Machine LearningOnsiteLLMAlignmentRLHF - SealthEasyMachine Learning EngineerRepresent k-means as an MLP
This question tests the conceptual mapping between the distance-based assignment step of k-means clustering and a single-layer neural network architecture.
Machine LearningOnsiteK-MeansNeural NetworksLinear Algebra - ShopifyMediumMachine Learning EngineerBuild model to predict package delivery time
This question evaluates a candidate's competence in end-to-end machine learning system design for time-to-event forecasting, covering prediction target definition and timing, feature engineering and model selection, handling missing or delayed signals and data leakage, uncertainty quantification, and production monitoring and retraining.
Machine LearningOnsite - ShopifyMediumMachine Learning EngineerModel Product Ranking
This question evaluates understanding of end-to-end machine learning ranking systems, including label definition, feature engineering, model selection, training strategies, and offline/online evaluation.
Machine LearningOnsite - SnapchatHardMachine Learning EngineerCompare Batch Normalization and Layer Normalization
Compare batch and layer normalization by their axes, learned parameters, running statistics, batch-size effects, and training-to-inference debugging.
Machine LearningOnsite - SnapchatMediumMachine Learning EngineerExplain BatchNorm, optimizers, and L1/L2
This question tests your conceptual understanding of how normalization, optimization strategies, and regularization techniques influence training stability, convergence, and model sparsity.
Machine LearningOnsiteDeep LearningOptimizationRegularization - SnapchatMediumMachine Learning EngineerExplain Core ML Concepts
This question tests the ability to explain foundational machine learning trade-offs and architectural nuances in deep neural networks.
Machine LearningOnsiteDeep LearningOptimizationStatistics - SnapchatHardMachine Learning EngineerExplain Neural-Network Regularization and Dropout
Explain L1, L2, dropout scaling, train/inference behavior, early stopping, and the difference between gradient penalties and decoupled weight decay.
Machine LearningOnsite - SnowflakeMediumMachine Learning EngineerCore Components of LLM Agent Memory: What Each Stores and How It Is Written and Read
Explain the common components of an LLM agent's memory, from the context window to session, episodic, semantic and procedural memory, and how each is written, retrieved and maintained. It tests agent architecture knowledge, context budgeting, conflict handling, forgetting and memory security.
Machine LearningOnsite - StackAdaptMediumMachine Learning EngineerFind the Flaws in an End-to-End ML Pipeline Case Study Report
Review a condensed end-to-end machine learning case study, from its goal and labels through normalization, evaluation, and deployment, and point out what is wrong with it. Tests systematic pipeline review, reasoning about what data each step uses, and ranking problems by how much they undermine the conclusions.
Machine LearningOnsite - SunriseEasySoftware EngineerExplain the least squares method
Explain the least squares method 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 LearningOnsite - TeslaMediumSoftware EngineerHow to Identify Best Battery Group
This question evaluates understanding of statistical inference, experimental design, and metric construction for binary test outcomes, including defining composite quality measures, handling unequal sample sizes, quantifying uncertainty, and addressing multiple-comparison issues.
Machine LearningOnsite - TikTokHardData ScientistDesign Real-Time Credit Card Fraud Detection System
Evaluates real-time credit-card fraud detection system design under latency, drift, and asymmetric costs. Strong answers cover data, delayed labels, features, models, serving architecture, monitoring, retraining, and thresholding.
Machine LearningOnsite - TikTokEasyData ScientistDesign multimodal deployment under compute limits
This question evaluates competency in Machine Learning systems engineering for a Data Scientist role, covering multimodal model deployment and inference optimization, scalable retrieval (ANN and hybrid search), generalization and regularization concepts (overfitting, dropout), normalization methods, and RLHF, emphasizing both theoretical principles and engineering feasibility. It is commonly asked to assess reasoning about trade-offs between quality, latency, and cost in resource-constrained environments, validation via offline/online metrics, and the ability to bridge conceptual understanding with practical deployment and system-level design considerations.
Machine LearningOnsite - TuroHardData ScientistCompare XGBoost and LightGBM
This question evaluates a data scientist's understanding of gradient-boosted tree implementations and practical model trade-offs, covering competencies in algorithmic differences, performance and memory characteristics, handling of missing and categorical features, regularization risks, and hyperparameter considerations for production models.
Machine LearningOnsite - Two SigmaHardData ScientistBuild a Housing Price Prediction Model from Historical Data
This question evaluates a data scientist's competency in end-to-end supervised machine learning for real-estate pricing, focusing on precise outcome definition, feature engineering, handling missing and time-varying data, and robustness to market regime changes.
Machine LearningOnsite
OpenAI
PayPal
Pinterest
Point72
Reddit
Salesforce
Samsara
Scale AI
Shopify
Snapchat
Snowflake
StackAdapt
Tesla
TikTok
Turo
Two Sigma