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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 571–600 of 782 results
- RobloxEasyMachine Learning EngineerFind the Most Frequent Log Call
This question evaluates a candidate's ability to perform frequency analysis on sequences, demonstrating competency with data structures for counting occurrences and with algorithmic time and space complexity considerations.
Coding & AlgorithmsTechnical Screen - RobloxEasyMachine Learning EngineerImplement recent-requests counter
This question evaluates a candidate's ability to design efficient in-memory data structures and manage time-windowed event counting, testing algorithmic reasoning and performance optimization for high-throughput services.
Coding & AlgorithmsTechnical Screen - RobloxMediumMachine Learning EngineerLevel-Ordered Dependency Build Order
This problem tests your ability to perform a Kahn’s algorithm-based topological sort while grouping tasks into parallelizable layers, ensuring cycle detection and dependency validation.
Coding & AlgorithmsTechnical ScreenGraphBFSTopological Sort - 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 - RunwayHardMachine Learning EngineerDesign a scalable video 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 a scalable video search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - RunwayHardMachine Learning EngineerImplement n-gram model and select n
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Implement n-gram model and select n states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - 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 - SamsungMediumMachine Learning EngineerValidate palindrome with constraints
This question evaluates a candidate's ability to implement string-processing algorithms and two-pointer techniques under O(n) time and O(1) extra space constraints, including character filtering, case normalization, edge-case handling, and algorithmic complexity analysis.
Coding & AlgorithmsTechnical Screen - Scale AIMediumMachine Learning EngineerDebug ML pipeline and build text parser
This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.
Data Manipulation (SQL/Python)Technical Screen - 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 AIHardMachine Learning EngineerExplain Transformers, attention, decoding, RL, and evaluation
This question evaluates understanding of transformer architectures, self- and cross-attention mechanisms, autoregressive decoding and sampling strategies, reinforcement learning–based fine-tuning (RLHF), evaluation methodologies, and training/inference optimization techniques.
Machine LearningTechnical Screen - 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 - Scale AIMediumMachine Learning EngineerImplement universal adversarial attack on GPT-2
Machine learning robustness prompt on safely evaluating universal adversarial prompts for GPT-2, covering threat modeling, differentiable optimization concepts, transferability, metrics, safety controls, and mitigation strategies without deployment-ready jailbreak details.
Machine LearningTechnical Screen - 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 a fraud detection model
Design an end-to-end machine learning system for real-time transaction fraud detection, covering label formulation under delayed feedback, feature design, threshold selection, and continuous monitoring.
Machine LearningTechnical Screenfraud-detectionmachine-learningsystem-design - ShopifyMediumMachine Learning EngineerBuild an Automatic Refund Decision System in a Pair-Programming Session
A pair-programming exercise to build an automatic refund system for an online store, combining light system design with working code across several incremental steps. It tests domain modeling, pluggable eligibility rules, preventing over-refunds and duplicate payouts on retries, and explaining design choices as you code.
Software Engineering FundamentalsTechnical Screen - 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 EngineerExplain motivation and role alignment
This question evaluates motivation, role alignment, and communication skills by asking a candidate to explain interest in the employer, educational choices, and career motivations as they relate to a Machine Learning Engineer role.
Behavioral & LeadershipHR Screen
Roblox
Salesforce
Samsara
Samsung
Scale AI
Shopify