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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.
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Showing 1–30 of 782 results
- AdobeHardMachine Learning EngineerDesign a natural-language AEP Q&A assistant
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 natural-language AEP Q&A assistant states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AdobeMediumMachine Learning EngineerDetermine if a multiple-sum subarray exists
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Determine if a multiple-sum subarray exists states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnsite - AdobeMediumMachine Learning EngineerDiscuss resume and behavioral scenarios
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss resume and behavioral scenarios states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - AdobeHardMachine Learning EngineerExplain Transformer Attention Fundamentals
This question tests the candidate's deep conceptual knowledge of the Transformer architecture, focusing on the mathematical mechanics of scaled dot-product attention, memory optimization, and hardware-aware training techniques.
Machine LearningOnsiteTransformersAttentionDeep Learning - AdobeMediumMachine Learning EngineerImplement K-means clustering from scratch
This problem tests your understanding of the K-means clustering algorithm, specifically the iterative process of centroid initialization, assignment, and update steps.
Coding & AlgorithmsTechnical ScreenK-meansClusteringUnsupervised Learning - AdobeHardMachine Learning EngineerOptimize LLM Training and Serving
This question tests the ability to diagnose hardware utilization bottlenecks in LLM workloads and design architectural optimizations to improve throughput and latency under constrained GPU memory resources.
ML System DesignOnsiteLLMGPU-ArchitecturePerformance-Engineering - AdyenEasyMachine Learning EngineerBuild a Payment Fraud Detection Model
Tests practical machine learning design for payment fraud detection, covering data preprocessing, temporal validation splits, class imbalance handling, and metric selection.
Machine LearningHR ScreenFraud DetectionClassificationFeature Engineering - AirbnbMediumMachine Learning EngineerDesign User Embedding Semantic Search
This question evaluates competency in ML system design for recommendation and semantic retrieval, covering user and listing representation learning, two-stage retrieval and ranking architectures, training objectives and label design, bias correction from interaction logs, and evaluation and experimentation practices.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerDesign a Family-Friendly Listing Classifier
This question evaluates proficiency in machine learning system design, including problem formulation tied to product objectives, labeling strategy, feature engineering, model selection, evaluation metrics, robustness to adversarial labels, fairness considerations, and cold-start handling.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerDesign photo and listing quality models
This question tests the end-to-end design of a machine learning system to evaluate the quality of listing photos and overall listing attractiveness to improve search ranking and conversion.
Machine LearningOnsiteComputer VisionRanking SystemsFeature Engineering - AirbnbMediumMachine Learning EngineerDiscuss ML Project Tradeoffs
This question evaluates a candidate's skills in model selection, trade-off analysis between precision, recall, latency and infrastructure cost, threshold tuning, reconciliation of offline versus online metrics, and comparative understanding of recommendation models within the Machine Learning domain.
Machine LearningOnsite - AirbnbMediumMachine Learning EngineerExplain ML Project Milestones, Scope, and Conflict
Explain an ML project through scope decisions, measurable milestones, stakeholder conflict, online readiness, and evidence-based changes to the plan.
Behavioral & LeadershipOnsite - AirbnbMediumMachine Learning EngineerGenerate Customer-Support Instructions from Company Policies
Design a policy-grounded support RAG system with ordered instructions, applicability checks, procedural evaluation, citations, and version-aware debugging.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerMinimize exact layover bookings
This problem tests your ability to use dynamic programming to determine if a specific target sum can be achieved using a subset of given item values and how to find the minimum subset size that hits that target.
Coding & AlgorithmsOnsiteDynamic ProgrammingOptimizationAlgorithms - AirbnbMediumMachine Learning EngineerMinimum Canisters to Top Up an Exact Target
Tests dynamic programming for the unbounded knapsack/coin change problem, focusing on finding the minimum number of canisters to reach an exact volume and reconstructing the optimal selection.
Coding & AlgorithmsTechnical Screendynamic-programmingknapsackarrays - AirbnbMediumMachine Learning EngineerRank Home Search Results Without a Text Query
Design a home-search ranker without query text, covering structured intent, training bias, cold start, online evaluation, and production debugging.
ML System DesignOnsite - AirbnbMediumMachine Learning EngineerRedact Document Phrases with Simultaneous Replacement
Redact document phrases with a replacement map, whole-word matching, longest-match overlap rules, and one-pass replacement of original text.
Coding & AlgorithmsOnsite - AirbnbMediumMachine Learning EngineerSolve Decimal Coin Change
This question evaluates proficiency in dynamic programming for coin-change problems and competency in numerical precision and safe handling of decimal monetary values in algorithmic implementations.
Coding & AlgorithmsOnsite - AirbnbMediumMachine Learning EngineerWhy Airbnb and what matters most
This question evaluates a candidate's alignment with organizational values, authenticity of motivation, and ability to articulate personal causes along with concrete actions that demonstrate impact and priorities.
Behavioral & LeadershipOnsite - AmazonMediumMachine Learning EngineerExplain Technical Contributions and Delivery Under a Tight Deadline
Explain a research or technical contribution and a deadline-driven delivery decision, with evidence, clear ownership, and honest limitations.
Behavioral & LeadershipTechnical Screen - AmazonMediumMachine Learning EngineerGRPO in NumPy: Group-Relative Advantages, Process Rewards and Shaped Rewards
Implement the core of Group Relative Policy Optimization in a NumPy notebook: group-normalized advantages, the clipped objective with a KL penalty, per-token credit from process rewards, and shaped rewards. Tests fluency with array axes and broadcasting and an understanding of how reward normalization interacts with shaping terms.
Machine LearningOnsite - AmazonMediumMachine Learning EngineerImplement Multi-Head Attention with a KV Cache and Cached Grouped-Query Attention
Implement causal multi-head attention from scratch, add a key-value cache for token-by-token decoding, and extend the forward pass to grouped-query attention, where several query heads share one key and value head. Tests tensor shape discipline, masking with cached positions, and reasoning about decode compute and cache memory.
Machine LearningOnsite - AmazonMediumMachine Learning EngineerPredict Click Probability for Music Recommendations
Design music recommendation CTR prediction with song embeddings, sparse-feature handling, model comparison, calibration, and online evaluation.
ML System DesignTechnical Screen - AmbienceHardMachine Learning EngineerDesign an ICD-10 Prediction System for Clinical Records
Design an end-to-end clinical NLP system to predict ICD-10 codes from long medical records, addressing extreme multi-label classification, long-tail code distributions, and human-in-the-loop coder feedback.
ML System DesignTechnical Screenml-system-designnlphealthcare - AmericanMediumMachine Learning EngineerDesign an enterprise RAG agent system
This question evaluates a candidate's competency in ML system design for enterprise retrieval-augmented generation and agent architectures, covering understanding of model roles, agent tool integrations, memory and state design, evaluation metrics, security and privacy defenses, concurrency and reliability, workflow durability, and document ingestion/indexing challenges. It is commonly asked in ML system design interviews to assess architectural thinking and trade-off analysis, testing both conceptual understanding and practical application skills for building scalable, secure, and cost-effective enterprise AI assistants.
ML System DesignTechnical Screen - AnthropicMediumMachine Learning EngineerBrainstorm How to Keep an AI Model Aligned With Its Intended Goals
Anthropic Fellows final-round practice: a 15-minute open discussion on defining, detecting and training against misalignment and misuse, ending with one falsifiable experiment.
Machine LearningOnsiteAnthropic FellowsAI Safety Fellowshipai-safety - AnthropicMediumMachine Learning EngineerConvert Samples into Event Intervals
This problem tests your ability to process sequential data, apply run-length encoding concepts, and implement filtering logic based on temporal constraints.
Coding & AlgorithmsTechnical ScreenArrayIntervalsData Processing - AnthropicMediumMachine Learning EngineerConvert State Stream to Events
This question tests the ability to process a stream of state changes, group consecutive identical values into distinct event intervals, and filter these events based on duration.
Coding & AlgorithmsOnsiteArraysData ProcessingAlgorithms - AnthropicMediumMachine Learning EngineerConvert stack samples to execution trace
This question evaluates understanding of sampled stack traces, event reconstruction, handling ambiguous or incomplete profiling data, and algorithmic reasoning about correctness and time/space complexity.
Coding & AlgorithmsTechnical Screen - AnthropicHardMachine Learning EngineerDebug a NumPy Extremely Randomized Trees Classifier
Anthropic Fellows debugging-OA practice: find the eight crash and accuracy bugs in a from-scratch NumPy Extra-Trees classifier until it matches scikit-learn.
Machine LearningOnline AssessmentAnthropic FellowsAI Safety Fellowshipdebugging
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