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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
- Languages
Showing 121–150 of 381 results
- 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 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 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 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 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 - MercorEasyMachine Learning EngineerDesign a Social Feed
Design an end-to-end personalized social feed system capable of serving low-latency ranked content while handling hybrid fanout, multi-stage ML candidate retrieval, and stable pagination.
System DesignTechnical Screensystem-designfeed-rankingrecommendation-systems - MercorMediumMachine Learning EngineerEvaluate Noisy Data for LLM Post-Training
Design a curation and validation framework to evaluate, filter, and safely integrate a large, noisy dataset into an LLM post-training pipeline without degrading model performance.
Machine LearningTechnical ScreenLLM Post-TrainingData CurationModel Evaluation - MetaMediumMachine Learning EngineerAnswer senior-level behavioral interview questions
This question evaluates leadership, decision-making, risk management, stakeholder management, and impact-communication skills for a senior Machine Learning Engineer role within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - MetaMediumMachine Learning EngineerBuild Friend Recommendations
This problem tests your ability to manipulate graph-like data structures to perform neighbor analysis and efficient set operations to rank potential connections.
Coding & AlgorithmsTechnical ScreenGraphsHash MapsSocial Network Analysis - MetaMediumMachine Learning EngineerCheck diagonal and compute window statistics
This question evaluates understanding of array and matrix manipulation, sliding-window and streaming statistics, and appropriate data-structure choices for maintaining aggregates and order-statistics such as moving averages and sliding-window medians.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerCheck diagonal equality in a matrix
This question evaluates skill in matrix manipulation and algorithmic reasoning, specifically understanding diagonal traversal, equality checks, and handling boundary conditions such as empty matrices, single-row or single-column inputs, and negative values.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerCompute sliding-window medians
This question evaluates proficiency in designing and analyzing efficient sliding-window algorithms and data structures for computing running medians, including correct handling of duplicates, deletions, and the convention for defining the median when k is even.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerDeep copy a linked list with random pointers
This problem tests your ability to manipulate complex pointer structures and maintain object identity across a deep copy operation in a non-linear linked list.
Coding & AlgorithmsTechnical ScreenLinked ListHash TablePointers - MetaMediumMachine Learning EngineerDerive Linear Regression Solution
This problem tests your ability to derive the Ordinary Least Squares (OLS) closed-form solution for a simple linear regression model and analyze the underlying mathematical assumptions.
Machine LearningTechnical ScreenLinear RegressionMathematicsMLE - MetaMediumMachine Learning EngineerDesign a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and Rewards
An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.
ML System DesignTechnical Screen - MetaMediumMachine Learning EngineerDesign a Short-Video Recommendation System
This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityRanking - MetaMediumMachine Learning EngineerDesign a weapon-ad harmful content detection system
This question assesses the ability to architect a scalable, multi-modal machine learning pipeline for detecting policy-violating content (weapon advertisements) within a high-throughput ad-delivery system.
System DesignTechnical ScreenMulti-modal MLComputer VisionNLP
LinkedIn
Luma AI
Lyft
Mercor
Meta