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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 31–60 of 351 results
- AnthropicMediumMachine Learning EngineerImplement a web crawler using a provided API
This problem tests your ability to design a multi-threaded web crawler that efficiently traverses a site while respecting domain constraints and avoiding redundant work.
Coding & AlgorithmsOnsiteConcurrencyGraph TraversalBFS - AnthropicMediumMachine Learning EngineerImplement an extensible prefix tree
This question evaluates the ability to design and implement an extensible prefix tree (trie), covering competencies in Unicode-aware string handling, memory and time optimization, concurrency or thread-safety considerations, and support for operations like insert, search, startsWith, countPrefix and erase.
Coding & AlgorithmsOnsite - AppleMediumMachine Learning EngineerAnalyze vision model failures
This question evaluates proficiency in computer vision machine learning fundamentals (including bias–variance tradeoffs, overfitting, class imbalance, evaluation metrics, threshold selection, calibration, and data leakage), probabilistic reasoning for performance interpretation, and production model diagnosis and root-cause analysis.
Machine LearningOnsite - AppleMediumMachine Learning EngineerDesign App Store search
This question evaluates a candidate's ability to design scalable, low-latency app marketplace search systems, covering competencies in query understanding, metadata ingestion and indexing, candidate retrieval and ranking, personalization, multilingual and typo-tolerant handling, cold-start and spam prevention, as well as online experimentation and monitoring. Commonly asked in the ML system design/search engineering domain to probe architectural trade-offs, measurement of product requirements and success metrics, and operationalization, it spans both high-level conceptual reasoning and practical application details.
ML System DesignOnsite - AppleMediumMachine Learning EngineerDesign CI/CD for AI Services
This question evaluates the ability to design a scalable CI/CD and MLOps platform that integrates backend services, model-serving APIs, data and feature pipelines, Kubernetes-based deployments, artifact and model versioning, security, and observability.
System DesignOnsite - AppleMediumMachine Learning EngineerDesign a grounded voice assistant
This problem evaluates your ability to design a retrieval-augmented generation (RAG) system for a voice assistant, focusing on grounding model outputs in trusted sources to prevent hallucinations.
ML System DesignOnsiteRAGLLMSystem Design - AppleMediumMachine Learning EngineerExplain classification lifecycle and CTR modeling
This question evaluates competency in end-to-end supervised classification and production machine learning systems, covering text feature engineering (bag-of-words), ensemble evaluation via out-of-bag methods, CTR prediction workflows including calibration and evaluation, and operational debugging of deployed models.
Machine LearningOnsite - AppleMediumMachine Learning EngineerHow do you prioritize and influence?
This question evaluates prioritization, stakeholder influence, resource negotiation, cross-functional collaboration, product judgment, and execution under constraints for an AI/ML engineering role.
Behavioral & LeadershipOnsite - AppleMediumMachine Learning EngineerImplement Kubernetes Service Dependency Queries
This question evaluates proficiency with data structures and algorithms for service inventory operations, including deterministic filtering, attribute matching, graph traversal for transitive dependency resolution, and status propagation across service graphs.
Coding & AlgorithmsOnsite - AppleMediumMachine Learning EngineerImplement permutations and image retrieval
This question evaluates algorithmic implementation and computational thinking across combinatorics, image processing, and vector similarity—specifically testing permutation generation, 2D convolution with explicit boundary handling, and nearest-neighbor retrieval for image embeddings.
Coding & AlgorithmsOnsite - AppleMediumMachine Learning EngineerOptimize image filters on device
This question evaluates competency in on-device machine learning and performance engineering, covering algorithm choice, numerical precision trade-offs, hardware utilization (CPU/GPU/accelerator), memory layout, tiling/streaming, and trade-offs among latency, memory overhead, and battery impact.
ML System DesignOnsite - AppleMediumMachine Learning EngineerSolve stock and banana problems
This question evaluates algorithmic problem-solving skills related to array processing, optimization under constraints, and reasoning about performance trade-offs by presenting a maximum single-transaction stock profit problem and a minimum banana-eating speed problem.
Coding & AlgorithmsOnsite - AtlassianMediumMachine Learning EngineerAssign bookings to minimum tennis courts
This problem tests your ability to manage overlapping time intervals using a greedy approach, specifically requiring the calculation of the maximum number of concurrent bookings to determine the minimum resource count.
Coding & AlgorithmsOnsiteGreedySortingIntervals - AtlassianHardMachine Learning EngineerCompute a moving average on a stream
This problem tests your ability to maintain a running average over a sliding window of size K in a high-throughput streaming environment while managing numerical precision and memory efficiency.
Coding & AlgorithmsOnsiteAlgorithmsData StructuresStreaming - AtlassianMediumMachine Learning EngineerCompute inverse-depth weighted sum of a nested list
This problem tests your ability to traverse nested list structures using recursion or iterative stacks and compute a value based on the nesting depth of individual elements.
Coding & AlgorithmsOnsiteRecursionTreesData Structures - AtlassianMediumMachine Learning EngineerCompute moving average over last N stream
This question evaluates understanding of streaming algorithms and sliding-window aggregation within the Coding & Algorithms domain, focusing on incremental computation, state management, and numerical stability when computing moving averages.
Coding & AlgorithmsOnsite - AtlassianMediumMachine Learning EngineerDesign Jira bug-to-team classification system
This question tests the ability to design an end-to-end ML pipeline for classifying incoming Jira tickets to appropriate engineering teams based on textual and historical metadata.
ML System DesignOnsiteNLPClassificationScalability - AtlassianMediumMachine Learning EngineerDesign O(1) cache and moving average
This problem tests your ability to maintain constant-time complexity for both cache management and sliding-window statistics by using a combination of a doubly-linked list and hash maps.
Coding & AlgorithmsOnsiteLRU CacheData StructuresSliding Window - AtlassianMediumMachine Learning EngineerDesign a Jira+Confluence RAG assistant
This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.
ML System DesignOnsite - AtlassianMediumMachine Learning EngineerDesign a scalable chatbot platform
This problem tests your ability to architect a scalable, stateful chatbot system that integrates RAG (Retrieval-Augmented Generation) with low-latency conversational requirements.
System DesignOnsiteLLMRAGScalability - AtlassianHardMachine Learning EngineerDesign an ML-powered search system
This question tests the ability to design a scalable, low-latency, and relevant search system for internal enterprise documents, focusing on the integration of hybrid search (keyword + semantic) and ranking strategies.
ML System DesignOnsiteInformation RetrievalRankingEmbeddings - AtlassianMediumMachine Learning EngineerDesign an internal data-access chatbot
This question evaluates a candidate's ability to design production-ready ML systems that integrate retrieval-augmented generation, secure access control, tool-based workflows, data privacy, auditability, and low-latency interaction.
ML System DesignOnsite - AtlassianMediumMachine Learning EngineerFind LCA in a tree and extend to a DAG
This problem tests your understanding of tree traversal, graph theory, and how to generalize path-finding algorithms from trees to directed acyclic graphs (DAGs).
Coding & AlgorithmsOnsiteGraphsLCATrees - AtlassianHardMachine Learning EngineerFind a secret word using match feedback
Design an optimal querying strategy to identify a secret word from a candidate vocabulary using exact-match character feedback under a strict query budget.
Coding & AlgorithmsOnsiteAlgorithmsMinimaxInformation Theory - ByteDanceHardMachine Learning EngineerCount Special Subarrays by Modular Frequency
This question evaluates the ability to apply modular arithmetic, frequency tracking, and prefix-count reasoning to efficiently count subarrays that satisfy a congruence condition, testing algorithmic problem-solving and scalable complexity management.
Coding & AlgorithmsOnsite - Capital OneMediumMachine Learning EngineerAnswer leadership scenarios with STAR
This question evaluates leadership, influence, problem-solving, prioritization, resilience, and the ability to deliver measurable results under constraints by eliciting STAR-format behavioral examples relevant to a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - Capital OneMediumMachine Learning EngineerDesign model deployment, monitoring, and low-latency inference
This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.
ML System DesignOnsite - Capital OneMediumMachine Learning EngineerExplain core ML concepts and lifecycle
This question evaluates understanding of core machine learning concepts and lifecycle competencies, including bias–variance tradeoff, parameters versus hyperparameters, batch versus real-time inference, strategies for training at scale, feature engineering, and end-to-end model development.
Machine LearningOnsite - Capital OneEasyMachine Learning EngineerHow would you improve card-type detection?
This question tests your ability to design a modular and maintainable classification system that identifies credit card networks (e.g., Visa, Mastercard) based on raw input data while considering validation and error handling.
Software Engineering FundamentalsOnsiteData ValidationString ManipulationSystem Design - Capital OneMediumMachine Learning EngineerReturn all root-to-leaf tree paths
This question evaluates competence with binary tree structures, path enumeration and traversal techniques along with the ability to represent traversal results as formatted strings.
Coding & AlgorithmsOnsite
Anthropic
Apple
Atlassian
Capital One