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Machine Learning Engineer 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 1–30 of 202 results
- AdobeHardMachine Learning EngineerExplain Transformer Attention Fundamentals
This question tests the candidate's deep conceptual knowledge of the Transformer architecture, focusing on the mathematical mechanics of scaled dot-product attention, memory optimization, and hardware-aware training techniques.
Machine LearningOnsiteTransformersAttentionDeep Learning - AdyenEasyMachine Learning EngineerBuild a Payment Fraud Detection Model
Tests practical machine learning design for payment fraud detection, covering data preprocessing, temporal validation splits, class imbalance handling, and metric selection.
Machine LearningHR ScreenFraud DetectionClassificationFeature Engineering - AirbnbMediumMachine Learning EngineerDesign photo and listing quality models
This question tests the end-to-end design of a machine learning system to evaluate the quality of listing photos and overall listing attractiveness to improve search ranking and conversion.
Machine LearningOnsiteComputer VisionRanking SystemsFeature Engineering - AirbnbMediumMachine Learning EngineerDiscuss ML Project Tradeoffs
This question evaluates a candidate's skills in model selection, trade-off analysis between precision, recall, latency and infrastructure cost, threshold tuning, reconciliation of offline versus online metrics, and comparative understanding of recommendation models within the Machine Learning domain.
Machine LearningOnsite - AmazonMediumMachine Learning EngineerGRPO in NumPy: Group-Relative Advantages, Process Rewards and Shaped Rewards
Implement the core of Group Relative Policy Optimization in a NumPy notebook: group-normalized advantages, the clipped objective with a KL penalty, per-token credit from process rewards, and shaped rewards. Tests fluency with array axes and broadcasting and an understanding of how reward normalization interacts with shaping terms.
Machine LearningOnsite - AmazonMediumMachine Learning EngineerImplement Multi-Head Attention with a KV Cache and Cached Grouped-Query Attention
Implement causal multi-head attention from scratch, add a key-value cache for token-by-token decoding, and extend the forward pass to grouped-query attention, where several query heads share one key and value head. Tests tensor shape discipline, masking with cached positions, and reasoning about decode compute and cache memory.
Machine LearningOnsite - 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 - 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 - 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 - 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 - 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 - 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 - ByteDanceMediumMachine Learning EngineerCompute 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 - ByteDanceMediumMachine Learning EngineerExplain 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 - ByteDanceMediumMachine Learning EngineerExplain 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 - ByteDanceHardMachine Learning EngineerImplement 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 - CadenceMediumMachine Learning EngineerExplain Transformer Positional Encoding
This question tests your conceptual and technical understanding of why Transformers require positional encodings and the mathematical mechanics of different encoding strategies.
Machine LearningTechnical ScreenTransformerAttentionNLP - Capital OneMediumMachine Learning EngineerExplain core ML concepts and lifecycle
This question evaluates understanding of core machine learning concepts and lifecycle competencies, including bias–variance tradeoff, parameters versus hyperparameters, batch versus real-time inference, strategies for training at scale, feature engineering, and end-to-end model development.
Machine LearningOnsite - CitadelHardMachine Learning EngineerMake a hard MoE router differentiable
Make a hard MoE router differentiable 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 - CohereMediumMachine Learning EngineerDefend Your Own Research Paper: Experimental Setup, Practicality and Later Work
A research paper round where you present your own published paper to an interviewer who already knows it and questions its weaknesses. Prepare to judge whether the experimental setup was reasonable, whether the results are practical, and what the field has learned since publication, all with specific, honest self-critique.
Machine LearningOnsite - CohereMediumMachine Learning EngineerExplain LLM training, RL, and evaluation
This question tests your conceptual understanding of the end-to-end LLM lifecycle, specifically the transition from raw text pre-training to alignment and evaluation.
Machine LearningOnsiteLLMRLHFPPO - CohereMediumMachine Learning EngineerExplain Top-k and Top-p Decoding
This question tests your conceptual and mathematical understanding of Top-k and Top-p (nucleus) sampling, focusing on how they modify the output probability distribution of autoregressive language models to balance creativity and coherence.
Machine LearningOnsiteNLPGenerative AISampling
Adobe
Airbnb
Amazon
Anthropic
Apple
Applied Intuition
Capital One
Citadel
Cohere