Machine Learning Interview Questions

Showing 31–60 of 348 results

• AmazonHard·Software Engineer
  Use a Fitted Line to Predict a Future Data Point

  This question tests the ability to implement a simple linear regression predictor and analyze the reliability of extrapolating time-series data using basic statistical heuristics.

  Machine LearningTechnical ScreenRegressionTime SeriesStatistics
• AAncestryMedium·Software Engineer
  Explain learning paradigms, loss, and embeddings

  This question evaluates understanding of core machine learning concepts—learning paradigms (supervised, unsupervised, self-supervised), loss functions, and embeddings—and the competency to explain their roles and interrelations in model training and inference.

  Machine LearningTechnical Screen
• AnthropicMedium·Software Engineer
  Debug a GRPO training loop and explain ratios

  This question evaluates debugging and implementation knowledge for on-policy reinforcement learning, focusing on GRPO/PPO-style training loops, importance-sampling ratios, log-prob computations, masking, and advantage normalization.

  Machine LearningTechnical Screen
• AppleMedium·Machine Learning Engineer
  Compare DCN v1 vs v2 and A/B test

  This question tests your understanding of architectural improvements in Deep & Cross Networks (DCN) and your ability to design a rigorous A/B testing strategy for model deployment in a high-stakes production environment.

  Machine LearningTechnical ScreenDeep LearningCTR PredictionRecommendation Systems
• AppleMedium·Machine Learning Engineer
  Compare audio preprocessing and training

  This question evaluates understanding of audio data preprocessing and end-to-end model training, assessing competencies in feature representation choices, normalization, segmentation, augmentation, and pipeline orchestration within machine learning for audio.

  Machine LearningTechnical Screen
• AppleMedium·Data Scientist
  Construct a Churn-Prediction Pipeline Using Scikit-Learn

  This question tests practical machine learning engineering skills, specifically the ability to construct an end-to-end classification pipeline for imbalanced tabular data. It evaluates knowledge of preprocessing, model validation, probability calibration, and production packaging — core competencies assessed in data scientist interviews.

  Machine LearningTechnical Screen
• AppleMedium·Data Scientist
  Design Siri-vs-GPT query routing

  This question evaluates a data scientist's competency in designing an end-to-end query-routing system between an on-device personal assistant and a large language model assistant, encompassing objectives, success metrics, ground-truth labeling, feature and model choices, ambiguity and multi-turn handling, and evaluation strategies.

  Machine LearningTechnical Screen
• AppleMedium·Machine Learning Engineer
  Explain Vision Encoders and LLM Bottlenecks

  This question evaluates understanding of vision encoders and their role in computer vision and multimodal models, familiarity with typical training approaches for encoders, and the ability to identify inference bottlenecks in large language models as well as knowledge of memory and latency optimization considerations.

  Machine LearningTechnical Screen
• AppleHard·Machine Learning Engineer
  Explain annotation agreement and LLM vs human judges

  This question tests a machine learning engineer's understanding of annotation reliability metrics and the trade-offs between human and LLM-based evaluation. It assesses conceptual and applied knowledge of inter-annotator agreement, metric selection across label types, and the validity of automated judges in model evaluation workflows.

  Machine LearningTechnical Screen
• AppleEasy·Machine Learning Engineer
  Implement Masked Multi-Head Self-Attention

  This question tests the ability to implement the core mechanics of a Transformer's multi-head attention mechanism, specifically focusing on linear projections, attention weight scaling, and causal masking.

  Machine LearningTechnical ScreenTransformersDeep LearningAttention
• Applied IntuitionMedium·Machine Learning Engineer
  Implement and explain positional encoding

  This question evaluates knowledge of positional encoding mechanisms for Transformer language models, covering embedding mathematics, tensor shapes and broadcasting, PyTorch implementation details, expected training and inference symptoms when positional information is omitted, and methods for empirical verification and ablation.

  Machine LearningTechnical Screen
• AtlassianMedium·Data Scientist
  Train and evaluate logistic model with regularization

  This question evaluates proficiency in supervised binary classification with logistic regression, implementation of L1 and L2 regularization, data preprocessing (missingness checks and standardization), hyperparameter selection via cross-validation, and model evaluation using ROC AUC and threshold-dependent metrics.

  Machine LearningTechnical Screen
• AAutodeskMedium·Machine Learning Engineer
  Explain 3D geometry data

  Explain 3D geometry data 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
• Bank of AmericaMedium·Data Scientist
  Explain core probability and ML statistics concepts

  This question evaluates core probability and machine learning statistics competencies including probability basics, descriptive statistics, correlation versus independence, linear regression and regularization, and dimensionality reduction (PCA), falling under the Machine Learning domain for Data Scientist roles and testing both conceptual understanding and practical application. Such multi-part theory questions are commonly asked to probe understanding of fundamental statistical concepts and modeling assumptions, ensuring the candidate can reason about uncertainty, estimator properties, multicollinearity, and eigenstructure without relying solely on implementation details.

  Machine LearningTechnical Screen
• BnpEasy·Data Scientist
  What are linear regression assumptions?

  Assesses understanding of the assumptions underlying ordinary least squares linear regression and the application of diagnostic methods to ensure unbiased parameter estimates and valid statistical inference.

  Machine LearningTechnical Screen
• Boston Consulting GroupMedium·Data Scientist
  Design and sample for credit default prediction

  This question evaluates a data scientist's competencies in imbalanced binary classification, temporal label definition and leakage detection, sampling and calibration under prior-probability shift, metric and operating-point selection, cost-sensitive decision rules, time-based validation and monitoring, and fairness and operational guardrails.

  Machine LearningTechnical Screen
• Boston Consulting GroupHard·Data Scientist
  Reduce overfitting under constraints

  This question evaluates a candidate's competency in machine learning for mitigating overfitting in tabular regression under production latency constraints, testing knowledge of regularization, model complexity control, feature engineering, validation strategies, and experimental design.

  Machine LearningTechnical Screen
• BByteDanceMedium·Software Engineer
  Choose Precision, Recall, or F1 for Agent Evaluation

  This question tests the ability to map business objectives and error costs to appropriate classification metrics in a real-world system context.

  Machine LearningTechnical ScreenMetricsClassificationModel Evaluation
• BByteDanceMedium·Data Scientist
  Choose Ranking Functions, Customer Value Metrics, and Predictive Models

  This problem tests the ability to implement SQL window functions for cohort analysis, define robust customer lifetime value metrics, and articulate the trade-offs between various machine learning algorithms in a production setting.

  Machine LearningTechnical ScreenSQLCustomer-AnalyticsModel-Selection
• BByteDanceMedium·Machine Learning Engineer
  Compute Sentence Similarity

  This question evaluates understanding of sentence-level and token-level embedding techniques, text preprocessing, similarity metrics, edge-case handling for empty or unknown tokens, and trade-offs between pretrained sentence encoders and averaged word embeddings.

  Machine LearningTechnical Screen
• BByteDanceMedium·Applied Scientist
  Explain GRPO for LLM Reinforcement Learning and How It Differs from PPO

  An ML question asking you to explain Group Relative Policy Optimization (GRPO) for fine-tuning large language models with reinforcement learning. It tests your grasp of group-normalized advantages, the clipped objective with a KL penalty, the contrast with PPO and its value model, and common failure modes.

  Machine LearningTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Explain LLM generational improvements, multimodal LLMs, and normalization in LLMs

  Three conceptual LLM questions: the main improvements across generations of a model family as described in technical reports, how multimodal image-and-text models are built and trained, and why modern LLMs use layer normalization or RMSNorm with pre-norm placement instead of batch normalization.

  Machine LearningTechnical Screen
• BByteDanceMedium·Machine Learning Engineer
  Explain XGBoost's Overfitting Resistance

  This question evaluates understanding of ensemble learning and model generalization, focusing on why gradient-boosted tree ensembles are often less prone to overfitting than single decision trees.

  Machine LearningTechnical Screen
• BByteDanceMedium·Data Scientist
  How would you choose a classification threshold?

  This question evaluates understanding of threshold selection for probabilistic binary classifiers, testing competencies in validation-set decision threshold choice, cost-sensitive trade-offs between false positives and false negatives, effects of class imbalance, and probability calibration.

  Machine LearningTechnical Screen
• BByteDanceMedium·Applied Scientist
  Implement Multi-Head Attention from Scratch

  An ML coding question asking you to implement multi-head attention from scratch in NumPy or PyTorch, from the query, key and value projections to the output projection. It tests tensor shape handling, scaled dot-product attention, masking, numerical stability and complexity analysis.

  Machine LearningTechnical Screen
• BByteDanceHard·Machine Learning Engineer
  Implement a Transformer Decoder from Scratch Without Starter Code

  Implement a transformer decoder from scratch with no starter code in about 30 minutes, including embeddings, masked multi-head self-attention, feed-forward layers, residual connections and layer normalization. Tests tensor-shape fluency, causal masking and hands-on deep learning coding for a machine learning engineer interview.

  Machine LearningTechnical Screen
• BByteDanceHard·Data Scientist
  Predict Short-Video Engagement with Skewed Data

  Design a short-video engagement model with explicit targets, leakage-safe features, heavy-tail handling, exposure awareness, and temporal evaluation.

  Machine LearningTechnical Screen
• BByteDanceMedium·Applied Scientist
  Write the Cross-Entropy Loss and Compute Precision, Recall and F1

  An ML fundamentals question in two parts: write the cross-entropy loss for binary and multi-class classification and explain why it is used, then compute precision, recall, F1 and accuracy for a described imbalanced classifier. It tests exact formulas, careful arithmetic and metric choice.

  Machine LearningTechnical Screen
• C3 AIMedium·Data Scientist
  Choose Tokenization and Dense or Sparse Retrieval

  This problem tests your understanding of document retrieval strategies, specifically comparing sparse and dense representations and how to integrate them into a production RAG system.

  Machine LearningTechnical ScreenRAGNLPInformation Retrieval
• C3 AIHard·Data Scientist
  Compare PCA and Autoencoders Under the Curse of Dimensionality

  This question tests your conceptual understanding of dimensionality reduction techniques and your ability to weigh their performance in high-dimensional feature spaces prone to the curse of dimensionality.

  Machine LearningTechnical ScreenDimensionality ReductionPCAAutoencoders