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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
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- 11
- Topics
- 8
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Showing 1–30 of 59 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 - 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 - 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 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 - 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 - 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 - CoinbaseMediumMachine Learning EngineerBuild a baseline classification model from messy data
This question evaluates skills in practical machine learning engineering, including data cleaning, preprocessing, feature selection, handling mixed numeric and categorical features, and baseline model construction for a binary classification task.
Machine LearningOnsite - DatabricksMediumMachine Learning EngineerImplement Gradient Descent Regression
This problem tests your understanding of linear regression by requiring the implementation of a gradient descent optimizer from scratch to minimize the Mean Squared Error loss.
Machine LearningOnsiteLinear RegressionGradient DescentOptimization - DatabricksMediumMachine Learning EngineerImplement Linear Regression Gradient Descent
This question tests the ability to implement batch gradient descent for simple linear regression from scratch, requiring the derivation of gradients for weights and bias, and understanding how to optimize convergence.
Machine LearningOnsiteGradient DescentLinear RegressionOptimization - DatabricksMediumMachine Learning EngineerImplement Simple Linear Regression with Gradient Descent from Scratch
Write simple linear regression trained with gradient descent from scratch, choosing the loss, learning rate and stopping rule yourself. Tests deriving the gradients correctly, writing a runnable training loop, understanding feature scaling and convergence, and verifying results against a closed-form fit.
Machine LearningOnsite - GoogleMediumMachine Learning EngineerCompare NLP tokenization and LLM recommendations
This question tests the conceptual understanding of tokenization strategies in NLP and the architectural trade-offs when integrating LLMs into recommendation systems.
Machine LearningOnsiteNLPLLMRecommendation-Systems - HarveyMediumMachine Learning EngineerTraining LLM Agents for Long-Context, Long-Horizon Tasks: Environments and Rewards
An ML research discussion on training language-model agents for long-context, long-horizon tasks. It tests the training recipe from context extension through supervised trajectories to RL, the design of reproducible training environments, and reward design that resists reward hacking over long trajectories.
Machine LearningOnsite - LinkedInMediumMachine Learning EngineerAnswer practical ML foundations questions
This question evaluates an interviewee's practical mastery of ML foundations, covering probability calibration and its evaluation, feature selection methods including neural-network approaches, tokenizer types and trade-offs in NLP, and optimizer behavior such as Adam's maintained statistics.
Machine LearningOnsite - MercorMediumMachine Learning EngineerReview a Compact GPT-Style Transformer Implementation
Assess and debug a compact decoder-only Transformer implementation. Candidates trace tensor dimensions through causal multi-head self-attention, identify flaws in next-token loss alignment and KV-cached generation, and establish equivalence between cached and uncached forward passes.
Machine LearningOnsiteTransformersPyTorchDeep Learning - MetaMediumMachine Learning EngineerBuild harmful-content text classifier
This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.
Machine LearningOnsiteNLPClassificationMLOps - MetaHardMachine Learning EngineerMachine Learning Fundamentals: Optimizers, Scaling Laws, and Clustering
Tests deep conceptual mastery of modern ML core foundations: AdamW vs. Adam weight decay mechanics, compute-optimal Chinchilla scaling laws, and the mathematical equivalence between Hard Expectation-Maximization in GMMs and k-means clustering.
Machine LearningOnsiteOptimizersScaling LawsClustering - MetaHardMachine Learning EngineerSelf-Attention: Implementation, Complexity, and Efficient Variants
Tests a candidate's mathematical and practical grasp of scaled dot-product attention, its computational and memory bottlenecks, and modern algorithmic optimizations like FlashAttention and linear attention.
Machine LearningOnsitetransformerattention-mechanismflashattention - MicrosoftMediumMachine Learning EngineerClean OCR data and build an LLM dataset
This problem tests the ability to design an automated pipeline for cleaning noisy OCR text output and preparing it for supervised fine-tuning of an LLM.
Machine LearningOnsiteData EngineeringOCRLLM Fine-tuning - MicrosoftMediumMachine Learning EngineerCompare preference alignment methods for LLMs
This question tests your conceptual understanding of alignment techniques, your ability to evaluate trade-offs between stability and performance, and your approach to measuring model safety and utility.
Machine LearningOnsiteRLHFDPOAlignment - MicrosoftMediumMachine Learning EngineerMeasure LLM Quality and Detect Hallucinations, Then Implement an Evaluation Component
A hands-on LLM evaluation exercise on measuring model quality, comparing prompt or model versions, and detecting hallucinations against supplied context. It also asks for a small Python groundedness scorer that flags unsupported sentences and aggregates a hallucination rate, testing evaluation judgment and clean, well-tested code.
Machine LearningOnsite - Mistral AIMediumMachine Learning EngineerAnswer an LLM Systems Quick-Fire Interview
This question tests your breadth of knowledge across the LLM stack, specifically focusing on inference optimization, evaluation strategies, and operational trade-offs.
Machine LearningOnsiteLLMInferenceOptimization - Mistral AIMediumMachine Learning EngineerOptimize Nearest-Center Assignment Without Materializing NxKxD Distances
This problem tests the ability to optimize memory usage in distance computations by leveraging algebraic expansion to avoid creating an intermediate (N, K, D) tensor.
Machine LearningOnsiteNumPyVectorizationLinear Algebra
Adobe
Airbnb
Amazon
Anthropic
Apple
Capital One
Cohere
Coinbase
Databricks
Google
Harvey
LinkedIn
Mercor
Meta
Microsoft
Mistral AI