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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 511–540 of 572 results
- Two SigmaMediumData ScientistPerform no-intercept linear regression from two datasets
This question evaluates linear regression modeling, numerical linear algebra (normal equations and stable factorization methods), regularization, R² interpretation, and data-preprocessing/validation skills within the Machine Learning domain for a Data Scientist role.
Machine LearningOnline Assessment - Two SigmaMediumData ScientistPredict Stock Prices from Google Search Data
This question evaluates a candidate's competency in time-series and cross-sectional predictive modeling, alternative-data feature engineering, and rigorous validation to prevent temporal leakage within the Machine Learning domain.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict a Company's Stock Price from Six Years of Web Search Logs
This question evaluates proficiency in feature engineering and time-series forecasting from large-scale event logs, including entity linking, aggregation and normalization, handling non-stationarity and drift, and model selection and validation for predicting a given company's stock price.
Machine LearningTechnical Screen - Two SigmaHardData ScientistPredict bike demand and avoid overfitting
This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.
Machine LearningTechnical Screen - Two SigmaHardData ScientistSlopes of y-on-x vs x-on-y Regression: Relationship and When They Are Equal
This question evaluates understanding of simple OLS linear regression estimators, moment-based expressions for slopes, the relationship between y-on-x and x-on-y fitted slopes, and how correlation and variable scaling influence coefficient values.
Machine LearningTechnical Screen - Two SigmaMediumData ScientistWhy Is the Sample Mean Approximately Normal for Large Samples?
This question evaluates understanding of the Central Limit Theorem and asymptotic statistical reasoning, including recognition that the standardized sample mean (equivalently the standardized sum) becomes approximately normal and how that fact underpins inference such as confidence intervals.
Machine LearningTechnical Screen - UberMediumData ScientistBuild cold-start restaurant ratings
This problem tests the ability to design an end-to-end machine learning system for predicting restaurant ratings when historical user-item interactions are scarce, focusing on feature engineering, model selection, and bias mitigation.
Machine LearningTechnical ScreenCold-StartRegressionFeature Engineering - UberHardData ScientistDesign ETA prediction for Uber rides
This question evaluates competency in end-to-end machine learning system design for real-time spatiotemporal ETA prediction, encompassing feature engineering from heterogeneous data sources, uncertainty quantification, model selection, and production serving and monitoring.
Machine LearningTechnical Screen - UberMediumData ScientistDesign a Ride-Hailing ETA System
This question evaluates a data scientist's competency in end-to-end machine learning system design for spatiotemporal prediction, covering temporal and geospatial feature engineering, model family selection, metric tradeoffs and calibration, selection bias and noise handling, latency and deployment constraints, monitoring, and experiment design.
Machine LearningTechnical Screen - UberMediumData ScientistEvaluate Promotions for Uber Eats Users
This question tests the ability to design an A/B test for promotions in a two-sided marketplace, focusing on identifying interference (network effects) and selecting appropriate metrics to measure causal impact.
Machine LearningTechnical ScreenCausal InferenceA/B TestingMarketplace Dynamics - UberEasyMachine Learning EngineerExplain and test completion-rate gaps
This question evaluates understanding of causal inference, product-metrics analysis, experimental design, and statistical hypothesis testing (including two-sample t-tests) as applied to operational problems in a marketplace.
Machine LearningTechnical Screen - UberMediumData ScientistHow would you build UberEats ranking?
This question assesses the ability to design an end-to-end ranking system for a food delivery platform, focusing on objective formulation, feature selection, and the trade-offs between relevance and business constraints.
Machine LearningTechnical ScreenRecommender SystemsLearning to RankFeature Engineering - UberEasyData ScientistHow would you design an ETA prediction system?
This problem tests the ability to design an end-to-end machine learning system for predicting ride arrival times, focusing on feature selection, model architecture, and evaluation strategies.
Machine LearningTechnical ScreenML System DesignRegressionFeature Engineering - UberMediumMachine Learning EngineerImplement 1D convex minimization in Python
An Uber Machine Learning Engineer technical-screen question on derivative-free 1D convex optimization: minimize a black-box convex function on a closed interval using only function evaluations. It tests method selection (golden-section vs ternary search), stopping criteria and tolerances, function-evaluation and time/space complexity analysis, and robust handling of flat regions, boundary optima, noisy evaluations, and numerical precision.
Machine LearningTechnical Screen - UberMediumMachine Learning EngineerImplement CLIP Contrastive Loss
This question evaluates understanding and implementation of contrastive representation learning concepts—specifically similarity matrices, symmetric image-text contrastive loss, normalization, temperature scaling, and label construction—testing competency in building losses for embedding alignment in the Machine Learning domain.
Machine LearningTechnical Screen - UberMediumMachine Learning EngineerImplement Multi-Head Self-Attention
This question tests the ability to implement the multi-head self-attention mechanism from scratch using PyTorch, focusing on tensor manipulation, linear projections, and the scaled dot-product attention formula.
Machine LearningTechnical ScreenPyTorchDeep LearningTransformers - UberMediumData ScientistImplement Streaming Clustering for Numbers
This question evaluates a data scientist's competency in online clustering for numeric streams, testing understanding of streaming algorithms, bounded-memory cluster summaries, online updates, and issues including initialization, convergence behavior, outlier handling, and concept drift.
Machine LearningOnsite - UberMediumMachine Learning EngineerImplement linear and logistic regression
This question evaluates understanding and hands-on implementation of linear and logistic regression, testing competencies in prediction functions, loss formulation, gradient computation, gradient descent training, regularization, and model selection within the Machine Learning domain (supervised learning and statistical modeling).
Machine LearningTechnical Screen - UiPathEasyMachine Learning EngineerExplain Core ML Fundamentals
This question tests your conceptual understanding of how fundamental components of neural networks and predictive models affect optimization, generalization, and performance.
Machine LearningTechnical ScreenGradient DescentRegularizationActivation Functions - UiPathMediumMachine Learning EngineerExplain Transformer Layers and FFN Rationale
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 Transformer Layers and FFN Rationale states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerLLM fundamentals: Transformer internals, beam search decoding, and LoRA fine-tuning
An LLM fundamentals question that asks you to explain Transformer internals such as scaled self-attention, multi-head attention, positional information and causal masking. It also covers how beam search decoding compares with greedy decoding and sampling, and how LoRA reduces the cost of fine-tuning.
Machine LearningTechnical Screen - UiPathMediumMachine Learning EngineerReduce overfitting and explain the mean squared error loss
A machine learning phone screen on how to diagnose and reduce overfitting, and on the mean squared error loss: its definition, what minimizing it estimates, its probabilistic interpretation, and when another loss works better. Tests fundamentals, mechanisms and trade-off reasoning.
Machine LearningHR Screen - UiPathMediumMachine Learning EngineerReliable LLM tool calling: format control, failure recovery, and runaway loops
An LLM agent question on tool calling: how a model invokes tools, how to keep tool calls in a valid format, how to make tool use stable and fix wrong calls, and how to handle tool-call loops that run too long. It tests practical reliability engineering for production agents.
Machine LearningTechnical Screen - UpstartEasyData ScientistDerive logistic regression objective and gradients
This question evaluates understanding of logistic regression, maximum likelihood estimation, cross-entropy (negative log-likelihood), L2 regularization, and the ability to compute model gradients for binary classification.
Machine LearningHR Screen - UpstartMediumData ScientistDesign Push-Notification System for Airport Surge Pricing
Design Push-Notification System for Airport Surge Pricing 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 - UpstartHardData ScientistDesign a Real-Time Personalized Ad Selection System
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 a Real-Time Personalized Ad Selection System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UpstartEasyData ScientistExplain L1 vs L2 and ridge vs lasso
This question evaluates understanding of regularization techniques in Machine Learning—specifically distinctions between L1 and L2 norms and their instantiation as Lasso and Ridge regression—covering effects on objective functions, parameter sparsity, geometric intuitions, handling of correlated features, and the role of feature scaling.
Machine LearningOnsite - UpstartHardData ScientistHow to Architect a Personalized Ads Serving System
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 How to Architect a Personalized Ads Serving System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - UpstartMediumData ScientistLeverage Existing Model for Low Credit Score Applicants
Leverage Existing Model for Low Credit Score Applicants 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 - VisaMediumSoftware EngineerAssemble a RAG Pipeline From Provided Python Interfaces and Explain Retrieval
Assemble a retrieval-augmented generation pipeline from provided Python interfaces, covering chunking, embedding, vector search and grounded prompting, then explain why embeddings and cosine similarity work for retrieval and when RAG beats fine-tuning or long context. It tests applied LLM engineering and clear reasoning with unfamiliar tooling.
Machine LearningOnsite
Two Sigma
Uber
UiPath
Upstart
Visa