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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 61–90 of 572 results
- AmazonMediumData ScientistWrite and explain gradient descent pseudocode
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.
Machine LearningOnsite - AncestryMediumSoftware EngineerExplain 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 - AnthropicMediumMachine Learning EngineerBrainstorm How to Keep an AI Model Aligned With Its Intended Goals
Anthropic Fellows final-round practice: a 15-minute open discussion on defining, detecting and training against misalignment and misuse, ending with one falsifiable experiment.
Machine LearningOnsiteAnthropic FellowsAI Safety Fellowshipai-safety - AnthropicMediumSoftware EngineerDebug 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 - AnthropicHardMachine Learning EngineerDebug a NumPy Extremely Randomized Trees Classifier
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.
Machine LearningOnline AssessmentAnthropic FellowsAI Safety Fellowshipdebugging - AnthropicMediumMachine Learning EngineerDesign a Double Descent Experiment
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.
Machine LearningHR Screendouble-descentgeneralizationlinear-models - AnthropicMediumMachine Learning EngineerExplain batch inference design
This question tests your ability to design a robust, production-grade batch inference pipeline, focusing on data consistency, system reliability, and scalability.
Machine LearningOnsiteSystem DesignMLOpsBatch Processing - AnthropicMediumMachine Learning EngineerFill In the Sampling and Decode Loop of an LLM Inference Notebook
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.
Machine LearningOnsiteAnthropic FellowsAI Safety Fellowshipllm-inference - AnthropicMediumSoftware EngineerIdentify the Most Important AI Safety Risk
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.
Machine LearningHR ScreenAI SafetyRLHFSycophancy - AnthropicHardSoftware EngineerImplement and analyze custom attention
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.
Machine LearningOnsite - AnthropicMediumSoftware EngineerImplement and derive backprop from scratch
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.
Machine LearningOnsite - AppleMediumMachine Learning EngineerAnalyze vision model failures
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.
Machine LearningOnsite - AppleMediumMachine Learning EngineerCompare 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 - AppleMediumMachine Learning EngineerCompare 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 - AppleMediumData ScientistConstruct 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 - AppleMediumData ScientistDesign 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 - AppleMediumData ScientistExplain CNN shapes, params, and trade-offs
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.
Machine LearningOnsiteCNNDeep LearningModel Architecture - AppleMediumMachine Learning EngineerExplain 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 - AppleHardMachine Learning EngineerExplain 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 - AppleMediumMachine Learning EngineerExplain classification lifecycle and CTR modeling
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.
Machine LearningOnsite - AppleEasyMachine Learning EngineerImplement 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 - AppleHardData ScientistImplement random forest with OOB and imbalance
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.
Machine LearningOnsite - Applied IntuitionMediumMachine Learning EngineerImplement 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 - AtlassianMediumData ScientistMinimize max L1 radius with k centers in 1D
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.
Machine LearningOnline Assessment - AtlassianMediumData ScientistTrain 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 - AutodeskMediumMachine Learning EngineerExplain 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 - Axq CapitalMediumSoftware EngineerExplain What a Neural Network Is and How It Learns
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.
Machine LearningHR Screen - Bank of AmericaMediumData ScientistExplain 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 - BnpEasyData ScientistWhat 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 GroupMediumData ScientistAchieve 0.95 precision via thresholding
This question evaluates probabilistic classifier calibration, threshold selection to achieve a target precision, handling severe class imbalance, and precision–recall based evaluation metrics.
Machine LearningOnline Assessment
Amazon
Anthropic
Apple
Applied Intuition
Atlassian
Bank of America
Bnp
Boston Consulting Group