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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
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- 11
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Showing 271–300 of 782 results
- LinkedInMediumMachine Learning EngineerDesign a Personalized Feed and Compare Ranking Objectives
Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.
ML System DesignTechnical Screenml-system-designrecommendation-systemslearning-to-rank - LinkedInMediumMachine Learning EngineerDesign a Skills inference system
This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.
ML System DesignOnsiteNLPEntity LinkingRecommendation Systems - LinkedInHardMachine Learning EngineerDesign a distributed key-value store
Design a distributed key-value store evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - LinkedInMediumMachine Learning EngineerDesign a system for LinkedIn Skills
This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.
ML System DesignOnsite - LinkedInMediumMachine Learning EngineerDesign an ML Forecasting and Pacing System for Ad Bidding
Design an ad forecasting and budget pacing architecture that combines predictive ML with deterministic budget guardrails to optimize campaign delivery across non-stationary traffic.
ML System DesignTechnical Screenml-system-designad-techtime-series - LinkedInMediumMachine Learning EngineerExplain Core ML Fundamentals
This question assesses core conceptual knowledge of supervised learning, optimization strategies, and regularization techniques used in model training.
Machine LearningTechnical ScreenOptimizationRegularizationDecision Trees - LinkedInMediumMachine Learning EngineerExplain gradient descent, learning rate, backpropagation, overfitting and batch size
A machine learning fundamentals screen on how neural networks are trained: how gradient descent updates parameters, what the learning rate and batch size change, how backpropagation computes gradients, and how to diagnose and fix underfitting and overfitting. Tests mechanics and practical training judgment, not definitions alone.
Machine LearningTechnical Screen - LinkedInEasyMachine Learning EngineerExplain overfitting vs underfitting and fixes
This question tests the conceptual understanding of the bias-variance tradeoff and the practical strategies used to improve model generalization.
Machine LearningTechnical ScreenModel EvaluationBias-Variance TradeoffRegularization - LinkedInMediumMachine Learning EngineerFind shortest word transformation with caching
Find shortest word transformation with caching evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - LinkedInMediumMachine Learning EngineerGenerate uniform 0–6 from biased coin
This question evaluates understanding of randomized algorithms, probability theory, and techniques for extracting uniform randomness from a biased source.
Coding & AlgorithmsOnsite - LinkedInMediumMachine Learning EngineerImplement K-Means and Explain Convergence
This problem tests the implementation of the K-means clustering algorithm from scratch, requiring an understanding of the iterative refinement process, distance metrics, and the underlying mathematical objective.
Coding & AlgorithmsTechnical ScreenClusteringUnsupervised LearningAlgorithms - LinkedInHardMachine Learning EngineerImplement a Text-Embedding Recommender Training Pipeline
Tests the ability to structure an end-to-end, leakage-resistant training pipeline for a text-embedding recommender, covering profile flattening, embedding caching, temporal splits, binary classification, and artifact serialization.
Machine LearningTechnical Screenrecommender-systemsfeature-engineeringnlp-embeddings - LinkedInMediumMachine Learning EngineerImplement alert queries and spike detection
This problem tests your ability to process a stream of events with time-based constraints, requiring an efficient sliding-window approach and the use of monotonic data structures for spike detection.
Coding & AlgorithmsOnsiteSliding WindowData StructuresStreaming - LinkedInMediumMachine Learning EngineerPlan and lead a large recommendation project
This Behavioral & Leadership question evaluates leadership, execution planning, cross-functional coordination, resourcing, and stakeholder communication skills in the context of machine learning recommendation product development.
Behavioral & LeadershipOnsite - LinkedInMediumMachine Learning EngineerSample index from probability distribution
This question tests the ability to implement a weighted random sampling algorithm efficiently using prefix sums and binary search to achieve sub-linear query time.
Coding & AlgorithmsOnsiteAlgorithmsProbabilityBinary Search - LinkedInMediumMachine Learning EngineerSample uniformly from a circle’s area
This question evaluates understanding of continuous probability distributions and geometric random sampling, testing whether a candidate recognizes how area-weighted uniformity differs from naive radial choices.
Coding & AlgorithmsOnsite - LinkedInMediumMachine Learning EngineerSort the values of a quadratic function applied to a sorted array in linear time
Given an integer array sorted in ascending order and the coefficients of a quadratic function, apply the function to every element and return the results in sorted order in linear time. Tests reasoning about the shape of a parabola, handling of positive, negative and zero leading coefficients, and duplicate values.
Coding & AlgorithmsTechnical Screen - LinkedInHardMachine Learning EngineerSum Every Element of an N-Dimensional Array Readable Only by Index Lists
Compute the sum of all elements of an array with any number of dimensions when the array exposes only its dimension sizes and a get method that reads one element from a full list of indices. It tests systematic enumeration of every index combination and exact handling of sums beyond the 32-bit range.
Coding & AlgorithmsTechnical Screen - LinkedInHardMachine Learning EngineerTight-Deadline Launch, Disagreeing With a Manager, and Guiding a Junior Scientist
A three-question behavioral segment from a machine learning engineer phone screen: a project launch under a tight deadline, a disagreement with your manager, and guiding a junior scientist to finish a piece of work. It tests prioritization, constructive disagreement, mentoring, and concise STAR storytelling under time pressure.
Behavioral & LeadershipTechnical Screen - LinkedInMediumMachine Learning EngineerTransform Biased and Uniform Random Bits Exactly
Tests the ability to design and mathematically prove exact random variate generators between biased and uniform bit sources, analyzing termination and expected sample complexity.
Statistics & MathTechnical ScreenProbabilityAlgorithmsSampling - Luma AIMediumMachine Learning EngineerSolve Seven Python Image and Data Tasks
This problem tests foundational proficiency in NumPy array manipulation, mathematical function implementation, and geometric logic, which are essential for low-level image processing tasks.
Coding & AlgorithmsTechnical ScreenNumPyImage ProcessingNumerical Stability - LyftMediumMachine Learning EngineerExplain Transformer and Fine-Tuning Basics
This question evaluates understanding of transformer architectures, embedding models, and transfer learning concepts such as pretraining and fine-tuning, testing competence in representation learning and model adaptation.
Machine LearningTechnical Screen - LyftMediumMachine Learning EngineerImplement K-Means Clustering from Scratch in Plain Python or NumPy
A live coding exercise that asks you to implement k-means clustering from scratch in Python, with NumPy optional, and make it pass the interviewer's test cases. It tests the assignment and update steps, initialization, convergence, empty clusters and writing runnable code under time pressure.
Machine LearningTechnical Screen - LyftMediumMachine Learning EngineerReason About an Intercept Shift in Logistic Regression
Tests understanding of how an additive shift to the intercept of a logistic regression model impacts predicted probabilities, ranking metrics (ROC-AUC), thresholded classification metrics (Recall, FPR, Precision), and calibration.
Machine LearningTechnical ScreenLogistic RegressionModel EvaluationClassification Metrics - LyftMediumMachine Learning EngineerTrain vs Test Error Diagnosis, Overfitting Fixes by Model Type, and Cross-Validation
A machine learning fundamentals screen that asks you to diagnose models from pairs of training and test error, including the unusual case where the test error is the lower one. It continues with curbing overfitting in linear, tree-based and neural network models, and with setting up validation splits and k-fold cross-validation.
Machine LearningTechnical Screen - MercorMediumMachine Learning EngineerBuild a Candidate Search System
Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.
ML System DesignOnsiteml system designinformation retrievalnlp - MercorMediumMachine Learning EngineerBuild an Ollama Eval Harness
This question evaluates a candidate's ability to design and implement a lightweight evaluation harness for a locally hosted language model, testing competencies in ML system design, service integration, metrics instrumentation, reliability engineering (timeouts, retries), concurrency control, configuration management, and extensibility.
ML System DesignTechnical Screen - MercorMediumMachine Learning EngineerCheck Whether Every Parcel Finds a Locker With Forward Overflow
Parcels arrive one at a time for a bank of numbered lockers, each requesting a preferred locker and overflowing to the next higher-numbered empty one if it's taken. Decide whether every parcel finds a locker. Tests precise rule-following and an efficient alternative to simulating forward scans one slot at a time.
Coding & AlgorithmsOnsiteunion-findsimulationarrays - MercorMediumMachine Learning EngineerDesign Candidate Search And LLM Evaluation Tasks
Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.
ML System DesignOnsite - MercorMediumMachine Learning EngineerDesign Work Orchestration for Machine-Learning Data Pipelines
Design a resilient, multi-tenant orchestration engine tailored for machine learning pipelines that manages versioned DAGs, heterogeneous compute routing, data quality gates, and atomic dataset publication.
ML System DesignOnsiteML InfrastructureSystem DesignWorkflow Orchestration
LinkedIn
Luma AI
Lyft
Mercor