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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.
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Showing 211–240 of 381 results
- OpenAIHardMachine Learning EngineerDesign an ML search system
This question evaluates system-design and information-retrieval competencies for machine-learning-powered enterprise document search, covering ingestion and indexing pipelines, hybrid lexical and semantic retrieval, feature generation and ranking, multi-tenant access control, and security/compliance.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign an ML search system with RAG
Design an ML search system with RAG evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - OpenAIMediumMachine Learning EngineerDesign an OOD detection system
This question tests the ability to design an end-to-end system for detecting out-of-distribution (OOD) data in production LLMs to ensure reliability and safety.
ML System DesignTechnical ScreenLLMMLOpsOOD - OpenAIHardMachine Learning EngineerDesign enterprise RAG search system
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDiagnose Transformer training and inference bugs
This question evaluates practical debugging skills for Transformer-based sequence models, targeting machine learning engineers who must diagnose intermittent shape/dtype errors and convergence failures simultaneously. It tests deep familiarity with the full training stack — data pipelines, attention masking, positional encoding, optimizer precision, and distributed setup — and the ability to reason about how a single root cause can manifest across multiple symptoms.
Machine LearningTechnical Screen - OpenAIMediumMachine Learning EngineerImplement 1NN with NumPy
Tests your ability to implement a fast, vectorized 1-Nearest Neighbor (1NN) classifier using NumPy, focusing on memory-efficient distance computations and tie-breaking.
Machine LearningTechnical Screennumpyvectorizationmachine-learning - OpenAIMediumMachine Learning EngineerImplement Masked Cross-Entropy with Label Smoothing
Implement a numerically stable, masked cross-entropy loss function with label smoothing and temperature scaling in NumPy, returning both the loss and analytical gradients.
Machine LearningTechnical Screenmachine-learningnumpydeep-learning - OpenAIHardMachine Learning EngineerImplement a 1-Nearest-Neighbor Classifier with a Manhattan-Distance Follow-Up
Implement a 1-nearest-neighbor classifier that labels each query point with the label of its closest training point under Euclidean distance, then extend it to Manhattan distance. Tests vectorized distance computation under memory limits, tie handling and how the choice of metric changes predictions.
Machine LearningTechnical Screen - OpenAIHardMachine Learning EngineerImprove classifier with noisy multi-annotator labels
This question evaluates a candidate's ability to analyze noisy multi-annotator labels, design label-aggregation and dataset-splitting strategies, and choose modeling and evaluation approaches for binary text classification.
Machine LearningTechnical Screen - OpenAIMediumMachine Learning EngineerModel Infection, Immunity, Delayed Death, and Burn Interventions on a Grid
Design and implement a discrete grid simulation modeling a five-stage disease progression with state countdowns, immunity, delayed mortality, and external burn interventions.
Software Engineering FundamentalsTechnical Screensimulationmatrixstate-machine - OpenAIHardMachine Learning EngineerSimulate Infection Spread on a Grid
Implement a discrete-time cellular automaton simulating epidemic spread on a 2D grid with multi-day infection states, immunity waning, and mortality rules.
Coding & AlgorithmsTechnical ScreenSimulationMatrixCellular Automata - OpenAIMediumMachine Learning EngineerSimulate Infection Spread with Immunity and Recovery
Implement a cellular automaton simulation modeling disease spread on a 2D grid with discrete cell states and timed recovery.
Coding & AlgorithmsTechnical ScreenGrid SimulationCellular AutomataBFS - OpenAIHardMachine Learning EngineerStreaming Entropy with Numerical Stability
Implement an online, numerically stable algorithm to compute Shannon entropy over an unbounded stream of categorical tokens with O(1) updates.
Coding & AlgorithmsTechnical Screenstreamingmathnumerical-computing - OpenAIHardMachine Learning EngineerTrain a classifier and analyze dataset
This question evaluates proficiency in end-to-end supervised machine learning workflows—covering data validation, exploratory data analysis, preprocessing, model training and selection, evaluation metrics and calibration, fairness assessments, and reproducibility—and tests competencies in applied machine learning engineering, statistical reasoning, and model governance within the Machine Learning domain. It is commonly asked in technical interviews because it verifies practical implementation skills and judgment for deploying reliable classifiers, aligning metrics to business goals and diagnosing performance and fairness across slices, with emphasis on practical application informed by conceptual understanding.
Machine LearningTechnical Screen - OpenAIMediumMachine Learning EngineerTrain and analyze a classifier
This question evaluates proficiency in end-to-end machine learning engineering, including exploratory data analysis, time-aware train/validation/test splitting, model baseline and improvement, class imbalance strategies, leakage-safe hyperparameter tuning, metric computation and calibration, error analysis, reproducible training pipelines with CLI/config/seed control, explainability (feature importance/SHAP and ablations), and documentation of risks, fairness checks and monitoring hooks. It is commonly asked to assess practical ability to manage the full ML lifecycle and data hygiene in production-like scenarios, testing applied Data Manipulation (SQL/Python) and Machine Learning competencies with an emphasis on practical application while also requiring conceptual understanding of evaluation, calibration and fairness.
Data Manipulation (SQL/Python)Technical Screen - OpenAIMediumMachine Learning EngineerVectorize One-Nearest-Neighbor and Express It as a Neural Forward Pass
Tests vectorized distance computation in array libraries and the mathematical equivalence between nearest-prototype classification and an affine neural network layer.
Machine LearningTechnical Screenvectorizationnearest-neighborslinear-algebra - PDT PartnersMediumMachine Learning EngineerList All Positive Divisors of an Integer in Ascending Order, Then Beat Square-Root Time
Given a positive integer up to one trillion, return every positive divisor in ascending order, each listed once. The task tests divisor enumeration, correct handling of perfect squares and 64-bit values, and a follow-up asking whether the running time can grow more slowly than the square root of n.
Coding & AlgorithmsTechnical Screen - PDT PartnersMediumMachine Learning EngineerProject Deep Dive: Low-Latency LSTM Inference at Scale and the LSTM vs Transformer Choice
A project deep dive on a small LSTM model served for large-scale, latency-sensitive inference. The candidate must explain the deployment hardware, latency requirement and use case, how inference latency was reduced, why an LSTM was chosen over a Transformer, and whether that choice still holds today.
Machine LearningTechnical Screen - PerplexityMediumMachine Learning EngineerTrain and improve a scikit-learn binary classifier
This problem tests your proficiency in the scikit-learn workflow, specifically focusing on data preprocessing, addressing class imbalance in imbalanced datasets, and selecting appropriate metrics beyond accuracy.
Machine LearningTechnical Screenscikit-learnImbalanced DataModel Evaluation - PinterestHardMachine Learning EngineerBias-Variance Tradeoff and Vanishing Gradients in Feedforward Networks
Tests theoretical and mathematical understanding of the bias-variance decomposition and the vanishing-gradient phenomenon during backpropagation in deep feedforward architectures.
Machine LearningTechnical ScreenDeep LearningBackpropagationModel Generalization - PinterestMediumMachine Learning EngineerDesign Hierarchical Permission Checks
This question evaluates understanding of hierarchical access control, tree data structures, and permission inheritance, testing competencies in data modeling and algorithmic reasoning.
Coding & AlgorithmsTechnical Screen - PinterestHardMachine Learning EngineerDesign an ads system to improve CTR
This question evaluates machine learning system design competency for improving ad click-through rate, testing understanding of metrics, end-to-end architecture, training data and embeddings, and operational issues like cold start, bias and delayed feedback within advertising and recommender domains.
ML System DesignTechnical Screen - PinterestMediumMachine Learning EngineerExplain bias–variance, overfitting, and vanishing gradients
This question tests the conceptual understanding of the bias-variance tradeoff, methods for diagnosing and preventing overfitting, and the theoretical causes and architectural solutions for vanishing gradients in deep learning.
Machine LearningTechnical ScreenDeep LearningBias-Variance TradeoffRegularization - PinterestEasyMachine Learning EngineerExplain learning-rate fluctuation and vanishing gradients
This question evaluates a candidate's understanding of optimization dynamics (effects of learning rate on training stability) and backpropagation-related issues (vanishing gradients) in deep neural networks, assessing competencies in training behavior and gradient propagation within the Machine Learning domain.
Machine LearningTechnical Screen - PinterestHardMachine Learning EngineerExplain overfitting and how to prevent it
This question evaluates understanding of overfitting, the relationship between training and validation performance and generalization, and knowledge of regularization concepts including differences between L1 and L2 penalties.
Machine LearningTechnical Screen - PinterestHardMachine Learning EngineerExplain overfitting, underfitting, and regularization
This question tests the conceptual understanding of the bias-variance tradeoff and the practical application of regularization to balance model complexity and generalization error.
Machine LearningTechnical ScreenBias-VarianceRegularizationGeneralization - PinterestMediumMachine Learning EngineerGenerate all hyperparameter combinations
This problem tests your ability to generate the Cartesian product of multiple lists, a fundamental task in grid search for hyperparameter tuning.
Coding & AlgorithmsTechnical ScreenCombinatoricsRecursionIteration - PinterestMediumMachine Learning EngineerImplement a Sparse Matrix Class
Design and implement a memory-efficient sparse matrix class supporting entry retrieval, updates, addition, and matrix multiplication.
Coding & AlgorithmsTechnical Screensparse-matrixhash-tablelinear-algebra - PinterestHardMachine Learning EngineerImplement a Stateful Search Autocomplete Session
Design an interactive autocomplete system that tracks query frequencies, returns the top 3 ranking prefix matches per keystroke, and commits completed queries to dynamic history.
Coding & AlgorithmsTechnical ScreenTrieHash TableDesign - PinterestEasyMachine Learning EngineerImplement string-based rounding without floats
This question evaluates proficiency in string-based numeric processing, arbitrary-precision arithmetic concepts, precise rounding rules including deterministic tie-breaking, and handling of sign and decimal edge cases, and falls under the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen
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