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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 271–300 of 351 results
- RobloxMediumMachine Learning EngineerDesign a Game Recommendation System
This problem tests your ability to design a multi-stage recommendation architecture tailored to a gaming platform, focusing on user-specific constraints like age-appropriateness and the challenges of the long-tail game distribution.
ML System DesignOnsiteRecommendation SystemsSystem DesignRanking - RobloxMediumMachine Learning EngineerDesign a game genre classifier
This question assesses the ability to design an end-to-end multi-modal machine learning system for classifying user-generated game content based on diverse input features and real-time serving requirements.
ML System DesignOnsiteMulti-modalNLPComputer Vision - RobloxMediumMachine Learning EngineerFind Windows Containing a Target
This problem tests your ability to efficiently filter and identify intervals within a collection that satisfy specific containment constraints using two-pointer or sliding window techniques.
Coding & AlgorithmsOnsiteArraysFilteringIntervals - RobloxMediumMachine Learning EngineerFind target-heavy sliding windows
This question tests the ability to implement an efficient sliding window algorithm to analyze frequency-based patterns within a data stream or array.
Coding & AlgorithmsOnsiteSliding WindowAlgorithmsArray - RogoMediumMachine Learning EngineerInfer the Smallest Valid Character Order from Sorted Password Hints
This problem tests your ability to model dependency constraints using a Directed Acyclic Graph (DAG) and apply Kahn's algorithm or DFS-based topological sorting to resolve ordering dependencies.
Coding & AlgorithmsOnsiteGraphTopological SortPriority Queue - RogoMediumMachine Learning EngineerManage Context Windows in an Applied AI System
This question tests the ability to design a robust context-management layer for an LLM-based application that balances limited token capacity against competing sources of information.
ML System DesignOnsiteLLMRAGSystem Design - SalesforceMediumMachine Learning EngineerExplain ML and LLM fundamentals
This question evaluates understanding of core machine learning and LLM competencies including evaluation metrics (F1 vs accuracy), differences between classification and regression, fair benchmarking, inference pipeline bottlenecks and optimization, productionization of AI services, and modern LLM concepts such as the Transformer architecture, context engineering, retrieval-augmented generation, grounding, and guardrails. It is commonly asked in Machine Learning interviews to assess both conceptual understanding and practical application, probing the candidate's ability to reason about evaluation trade-offs, system-level performance constraints, and deployment considerations rather than only algorithmic detail.
Machine LearningOnsite - SamsaraMediumMachine Learning EngineerDiscuss salary expectations
This question evaluates a candidate's ability to communicate compensation expectations, justify a targeted base salary through market benchmarking and assumptions, and articulate openness to trade-offs for a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - SamsaraMediumMachine Learning EngineerExplain LLM training and evaluation
This question evaluates an engineer's competency in LLM engineering, including transformer pretraining and finetuning, alignment approaches like instruction tuning and RLHF, strategies for reducing hallucinations, evaluation and production monitoring practices, and inference optimizations.
Machine LearningOnsite - SamsungMediumMachine Learning EngineerMaximize sum of non-adjacent values
This question evaluates algorithmic problem-solving skills, specifically understanding of dynamic programming and optimization when selecting non-adjacent elements, within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - Scale AIEasyMachine Learning EngineerExplain LLM post-training methods and tradeoffs
This question assesses the candidate's understanding of the LLM post-training pipeline, specifically how to transition a foundation model into a helpful, safe assistant through various alignment techniques.
Machine LearningOnsiteLLMAlignmentRLHF - Scale AIEasyMachine Learning EngineerHandle customer engagement and manager-rating questions
This question evaluates stakeholder management, leadership principles, conflict-resolution skills, and self-awareness, emphasizing interpersonal communication, influence, and accountability.
Behavioral & LeadershipOnsite - Scale AIEasyMachine Learning EngineerImplement multi-head attention and LLM sampling
This problem tests your ability to implement the core mechanics of a Transformer's attention mechanism and the logic required to sample the next token from a model's output logits.
Coding & AlgorithmsOnsiteTransformersDeep LearningNumPy - SealthEasyMachine Learning EngineerImplement one-cluster k-means
This question tests the ability to implement a one-cluster k-means algorithm (finding the geometric centroid) and understanding of the mathematical property that minimizes sum of squared errors.
Coding & AlgorithmsOnsiteK-MeansClusteringStatistics - SealthEasyMachine Learning EngineerRepresent k-means as an MLP
This question tests the conceptual mapping between the distance-based assignment step of k-means clustering and a single-layer neural network architecture.
Machine LearningOnsiteK-MeansNeural NetworksLinear Algebra - ShopifyMediumMachine Learning EngineerBuild model to predict package delivery time
This question evaluates a candidate's competence in end-to-end machine learning system design for time-to-event forecasting, covering prediction target definition and timing, feature engineering and model selection, handling missing or delayed signals and data leakage, uncertainty quantification, and production monitoring and retraining.
Machine LearningOnsite - ShopifyHardMachine Learning EngineerCollect labels without existing data
This question evaluates a machine learning engineer's competency in label definition and leakage prevention, instrumentation and logging design, programmatic and human-in-the-loop labeling pipelines, experimental approaches for eliciting outcomes, bias mitigation, and continuous label quality monitoring.
Analytics & ExperimentationOnsite - ShopifyMediumMachine Learning EngineerDescribe an end-to-end ML project
This question evaluates leadership and technical competencies in end-to-end machine learning project execution—specifically project management, cross-functional stakeholder coordination, ML system design, data engineering, modeling, evaluation, and production monitoring; it sits in the Behavioral & Leadership category and the domain of machine learning systems and product analytics, testing practical application of these skills. It is commonly asked to determine a candidate's ability to translate business objectives into measurable ML solutions, reason about trade-offs across metrics, data, modeling and infrastructure, and demonstrate both conceptual understanding and hands-on operationalization.
Behavioral & LeadershipOnsite - ShopifyMediumMachine Learning EngineerDesign Personalized Product Feeds
This question evaluates a candidate's ability to design end-to-end machine learning systems for personalized product recommendation, assessing competencies in data collection, candidate generation, feature pipelines, model training, online serving, and re-ranking.
ML System DesignOnsite - ShopifyHardMachine Learning EngineerDesign a baseline loan recommendation system
This question evaluates understanding of ML-driven recommendation and credit-risk systems, including skills in personalization, feature design, risk calibration, fairness and regulatory compliance, explainability, and operational monitoring.
ML System DesignOnsite - ShopifyHardMachine Learning EngineerDesign a hierarchical multi-label classifier
This question evaluates a candidate's competency in designing production-grade hierarchical multi-label classification systems, covering taxonomy normalization, label dependency modeling, loss and decoding strategies, class imbalance handling, evaluation metrics, data labeling at scale, and operational constraints such as latency, model size, and monitoring. Commonly asked in the ML System Design domain to assess both conceptual understanding of hierarchical label structures and practical application-level trade-offs in architecture, training, deployment, and maintenance.
ML System DesignOnsite - ShopifyMediumMachine Learning EngineerDesign search autocomplete ML system
This question evaluates an engineer's ability to design an ML-powered search autocomplete system, testing competencies in candidate generation versus ranking architectures, data sourcing and labeling, feature and model selection (including personalization and context), offline training and online serving, experimentation and monitoring, and safety and abuse mitigation within information retrieval. Commonly asked to assess how applicants balance production concerns such as latency, relevance, scalability, cold-start handling, and feedback-loop mitigation, it sits in the ML system design domain and emphasizes practical, applied engineering trade-offs rather than purely conceptual theory.
ML System DesignOnsite - ShopifyMediumMachine Learning EngineerImplement URL Shortening Codec
This question evaluates understanding of hashing, encoding, state management, and collision-handling strategies for creating a consistent mapping between long and short URLs, testing competencies in data structures, algorithms, and simple system design within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - ShopifyMediumMachine Learning EngineerImplement an interactive CLI class with tests
This question evaluates object-oriented design, command-line interface implementation, persistent state management, input validation and error logging, extensibility for new commands, comprehensive unit testing, and continuous integration configuration.
Coding & AlgorithmsOnsite - ShopifyMediumMachine Learning EngineerModel Product Ranking
This question evaluates understanding of end-to-end machine learning ranking systems, including label definition, feature engineering, model selection, training strategies, and offline/online evaluation.
Machine LearningOnsite - SnapchatHardMachine Learning EngineerCompare Batch Normalization and Layer Normalization
Compare batch and layer normalization by their axes, learned parameters, running statistics, batch-size effects, and training-to-inference debugging.
Machine LearningOnsite - SnapchatMediumMachine Learning EngineerDesign a Product Tagging Pipeline
This problem tests your ability to design a scalable multi-modal machine learning pipeline that processes diverse content types to generate standardized taxonomy tags for an e-commerce platform.
ML System DesignOnsiteComputer VisionNLPMulti-modal - SnapchatMediumMachine Learning EngineerDesign a Trustworthy Ranking System
This problem tests the ability to design an end-to-end ML system for ranking content while balancing engagement metrics with safety, misinformation, and user trust.
ML System DesignOnsiteRankingTrust & SafetyMulti-objective Optimization - SnapchatHardMachine Learning EngineerDesign a harmful content detection system
This question evaluates proficiency in ML system design for harmful-content detection, assessing competencies in multimodal modeling, taxonomy and labeling strategies, data strategy, privacy-aware architecture, real-time inference, human-in-the-loop decisioning, and operational reliability for global consumer platforms.
ML System DesignOnsite - SnapchatMediumMachine Learning EngineerDesign a search-to-ads ranking pipeline
This question tests the ability to design a high-throughput, low-latency ranking system that balances user experience with monetization goals through multi-stage retrieval and bidding mechanics.
System DesignOnsiteRankingRecommendation-SystemsAd-Tech
Roblox
Salesforce
Samsara
Samsung
Scale AI
Shopify
Snapchat