Machine Learning Interview Questions

Showing 151–163 of 163 results

• UberMedium·Data Scientist
  Implement 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
• UpstartEasy·Data Scientist
  Explain 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
• UpstartMedium·Data Scientist
  Leverage 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
• VisaMedium·Software Engineer
  Assemble 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
• Walmart LabsEasy·Machine Learning Engineer
  Design an LLM agent with RAG and tools

  This problem tests the ability to architect an LLM-based agent capable of retrieving information and performing actions, focusing on orchestration, tool execution, and system reliability.

  Machine LearningOnsiteLLMRAGAgentic-Workflow
• Walmart LabsMedium·Data Scientist
  Predict Next-Period Conversion Rate Using Historical Campaign Data

  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 Predict Next-Period Conversion Rate Using Historical Campaign Data states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  Machine LearningOnsite
• WayfairMedium·Applied Scientist
  Simulate and Compare Multi-Armed Bandit Strategies

  Compare epsilon-greedy, UCB1, and Thompson sampling in a reproducible Bernoulli bandit simulation with reward, pseudo-regret, and repeated-run uncertainty.

  Machine LearningOnsite
• WaymoMedium·Software Engineer
  Design an Online Experiment

  This question tests the ability to design a statistically sound A/B test for a machine learning model update in a real-world, dynamic environment, focusing on metric selection and mitigation of interference.

  Machine LearningOnsiteExperimentationA/B TestingCausal Inference
• WaymoEasy·Data Scientist
  How predict vehicles’ turn direction at intersection?

  This problem tests the ability to design an end-to-end machine learning pipeline for vehicle intent prediction, focusing on feature engineering, sequence modeling, and temporal validation.

  Machine LearningOnsiteTime-SeriesFeature EngineeringAutonomous Driving
• YYelpHard·Data Scientist
  Design a Cold-Start-Aware Recommender

  This question evaluates a data scientist's expertise in production recommender systems, covering cold-start strategies, two-stage retrieval and ranking model design, multimodal feature engineering, exploration–exploitation, bias-corrected experimentation, and low-latency serving.

  Machine LearningOnsite
• ZAZest AIHard·Data Scientist
  Build a fair loan classifier

  This question evaluates competency in supervised classification and credit risk modeling, covering data exploration and preparation, model selection, evaluation metrics, threshold setting, fairness and regulatory considerations, interpretability, validation, and monitoring.

  Machine LearningOnsite
• ZAZest AIHard·Data Scientist
  Diagnose overfitting from error curves

  This question evaluates the ability to recognize and diagnose model generalization problems—specifically overfitting—by interpreting training, validation, and test error curves and understanding validation procedures and sources of variance.

  Machine LearningOnsite
• xAIMedium·Machine Learning Engineer
  Explain Data Collection Choices and Their Impact on Downstream Model Performance

  A machine learning screening question: explain how the data for a project on your resume was collected and processed, then discuss how data choices affect performance on downstream tasks. It tests first-hand knowledge of data pipelines, causal reasoning about data quality, and how to measure data effects.

  Machine LearningOnsite