Machine Learning interview questions

Showing 61–90 of 572 results

• Write and explain gradient descent pseudocodeMedium

  This question evaluates a candidate's understanding of batch gradient descent for linear regression with an intercept, covering concepts such as mean squared error optimization, vectorized matrix operations, analytical gradient computation, learning rate schedules, stopping criteria, feature scaling, and L2 regularization.

  AmazonData ScientistMachine LearningOnsite
• Explain learning paradigms, loss, and embeddingsMedium

  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.

  AncestrySoftware EngineerMachine LearningTechnical Screen
• Brainstorm How to Keep an AI Model Aligned With Its Intended GoalsMedium

  Anthropic Fellows final-round practice: a 15-minute open discussion on defining, detecting and training against misalignment and misuse, ending with one falsifiable experiment.

  AnthropicMachine Learning EngineerMachine LearningOnsite
• Debug a GRPO training loop and explain ratiosMedium

  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.

  AnthropicSoftware EngineerMachine LearningTechnical Screen
• Debug a NumPy Extremely Randomized Trees ClassifierHard

  Anthropic Fellows debugging-OA practice: find the eight crash and accuracy bugs in a from-scratch NumPy Extra-Trees classifier until it matches scikit-learn.

  AnthropicMachine Learning EngineerMachine LearningOnline Assessment
• Design a Double Descent ExperimentMedium

  Tests your ability to design a controlled synthetic experiment demonstrating double descent, explain the mathematical causes of test error divergence near the interpolation threshold, and analyze the interplay between noise, conditioning, and regularization.

  AnthropicMachine Learning EngineerMachine LearningHR Screen
• Explain batch inference designMedium

  This question tests your ability to design a robust, production-grade batch inference pipeline, focusing on data consistency, system reliability, and scalability.

  AnthropicMachine Learning EngineerMachine LearningOnsite
• Fill In the Sampling and Decode Loop of an LLM Inference NotebookMedium

  Anthropic Fellows final-round practice: complete temperature/top-k/top-p sampling and a batched KV-cache decode loop in a Colab notebook so greedy output matches model.generate.

  AnthropicMachine Learning EngineerMachine LearningOnsite
• Identify the Most Important AI Safety RiskMedium

  Tests your ability to analyze AI alignment failure modes—specifically sycophancy and emotional over-reliance—and formulate rigorous evaluation metrics, training mitigations, and product-level guardrails.

  AnthropicSoftware EngineerMachine LearningHR Screen
• Implement and analyze custom attentionHard

  This question evaluates implementation and analysis skills for scaled dot-product attention, testing competencies in efficient tensorized PyTorch coding, numerical stability and mixed-precision handling, masking semantics (causal and padding), multi-head shape correctness, and unit-test validation including gradient and edge-case behavior.

  AnthropicSoftware EngineerMachine LearningOnsite
• Implement and derive backprop from scratchMedium

  This question evaluates understanding and practical implementation of neural network fundamentals, specifically analytic backpropagation and gradient derivation, numerically stable binary cross-entropy computation, parameter initialization, gradient descent updates, and gradient checking.

  AnthropicSoftware EngineerMachine LearningOnsite
• Analyze vision model failuresMedium

  This question evaluates proficiency in computer vision machine learning fundamentals (including bias–variance tradeoffs, overfitting, class imbalance, evaluation metrics, threshold selection, calibration, and data leakage), probabilistic reasoning for performance interpretation, and production model diagnosis and root-cause analysis.

  AppleMachine Learning EngineerMachine LearningOnsite
• Compare DCN v1 vs v2 and A/B testMedium

  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.

  AppleMachine Learning EngineerMachine LearningTechnical Screen
• Compare audio preprocessing and trainingMedium

  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.

  AppleMachine Learning EngineerMachine LearningTechnical Screen
• Construct a Churn-Prediction Pipeline Using Scikit-LearnMedium

  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.

  AppleData ScientistMachine LearningTechnical Screen
• Design Siri-vs-GPT query routingMedium

  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.

  AppleData ScientistMachine LearningTechnical Screen
• Explain CNN shapes, params, and trade-offsMedium

  This question tests your ability to calculate output dimensions, parameter counts, and computational costs (FLOPs/MACs) in CNNs, while requiring you to justify architectural choices like depthwise convolutions and normalization layers.

  AppleData ScientistMachine LearningOnsite
• Explain Vision Encoders and LLM BottlenecksMedium

  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.

  AppleMachine Learning EngineerMachine LearningTechnical Screen
• Explain annotation agreement and LLM vs human judgesHard

  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.

  AppleMachine Learning EngineerMachine LearningTechnical Screen
• Explain classification lifecycle and CTR modelingMedium

  This question evaluates competency in end-to-end supervised classification and production machine learning systems, covering text feature engineering (bag-of-words), ensemble evaluation via out-of-bag methods, CTR prediction workflows including calibration and evaluation, and operational debugging of deployed models.

  AppleMachine Learning EngineerMachine LearningOnsite
• Implement Masked Multi-Head Self-AttentionEasy

  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.

  AppleMachine Learning EngineerMachine LearningTechnical Screen
• Implement random forest with OOB and imbalanceHard

  This question evaluates expertise in implementing and engineering a memory- and compute-efficient Random Forest for binary classification, covering ensemble methods, CART/Gini impurity, OOB evaluation and calibration, class-imbalance handling, reproducibility, parallel and thread-safe system design, and streaming/warm-start model updates.

  AppleData ScientistMachine LearningOnsite
• Implement and explain positional encodingMedium

  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.

  Applied IntuitionMachine Learning EngineerMachine LearningTechnical Screen
• Minimize max L1 radius with k centers in 1DMedium

  This question evaluates algorithmic problem-solving skills and understanding of clustering and optimization concepts, specifically the 1D k-center (minimax L1) problem, greedy feasibility tests, binary-search-on-answer techniques, numerical stability with large coordinates, and time/space complexity analysis relevant for a Data Scientist role.

  AtlassianData ScientistMachine LearningOnline Assessment
• Train and evaluate logistic model with regularizationMedium

  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.

  AtlassianData ScientistMachine LearningTechnical Screen
• Explain 3D geometry dataMedium

  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.

  AutodeskMachine Learning EngineerMachine LearningTechnical Screen
• Explain What a Neural Network Is and How It LearnsMedium

  Explain what a neural network is: how units and layers turn weighted inputs into outputs, why non-linear activations are essential, and how the weights are learned with a loss function, backpropagation, and gradient descent. It tests core machine learning fundamentals, including overfitting and when simpler models are the better choice.

  Axq CapitalSoftware EngineerMachine LearningHR Screen
• Explain core probability and ML statistics conceptsMedium

  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.

  Bank of AmericaData ScientistMachine LearningTechnical Screen
• What are linear regression assumptions?Easy

  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.

  BnpData ScientistMachine LearningTechnical Screen
• Achieve 0.95 precision via thresholdingMedium

  This question evaluates probabilistic classifier calibration, threshold selection to achieve a target precision, handling severe class imbalance, and precision–recall based evaluation metrics.

  Boston Consulting GroupData ScientistMachine LearningOnline Assessment