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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 381 results
- 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 - 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 - 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 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 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 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 - AnthropicMediumMachine Learning EngineerDescribe communication to resolve ambiguity
Describe communication to resolve ambiguity evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AnthropicHardMachine Learning EngineerDesign a scalable MapReduce pipeline
Design a scalable MapReduce pipeline 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 - AnthropicMediumMachine Learning EngineerImplement cluster status tracker
This question evaluates a candidate's competence in data structures and algorithms for time-ordered state tracking, time-series indexing, conflict resolution (last-write-wins), memory-efficient storage, and concurrent system behavior under high update rates.
Coding & AlgorithmsTechnical Screen - AnthropicMediumMachine Learning EngineerOptimize MapReduce performance
This question evaluates understanding of the MapReduce programming model and distributed batch processing at scale. It tests practical knowledge of parallel efficiency, network bottleneck analysis, and optimization techniques like combiners, partitioning, and speculative execution — core competencies assessed in machine learning and data engineering roles.
System DesignTechnical Screen - AnthropicHardMachine Learning EngineerSchedule Large and Small Model Inference on an Eight-GPU Pool
Design an inference scheduler for a shared 8-GPU node that co-locates single-GPU small models and all-reduce 8-GPU large models. The design must address gang scheduling, prefix cache reuse, deadline-aware priorities, and overload mitigation.
ML System DesignTechnical Screenml-system-designgpu-schedulingdistributed-inference - AppleMediumMachine Learning EngineerChoose File Storage Tiers Using Access Predictions
Design an end-to-end machine learning and decision system that predicts file access likelihood to automatically transition cloud-stored user files across storage tiers to minimize operational cost while meeting retrieval latency SLAs.
ML System DesignTechnical Screenml-system-designprobability-calibrationcloud-storage - AppleMediumMachine Learning EngineerCompare DCN v1 vs v2 and A/B test
This question tests your understanding of architectural improvements in Deep & Cross Networks (DCN) and your ability to design a rigorous A/B testing strategy for model deployment in a high-stakes production environment.
Machine LearningTechnical ScreenDeep LearningCTR PredictionRecommendation Systems - AppleMediumMachine Learning EngineerCompare audio preprocessing and training
This question evaluates understanding of audio data preprocessing and end-to-end model training, assessing competencies in feature representation choices, normalization, segmentation, augmentation, and pipeline orchestration within machine learning for audio.
Machine LearningTechnical Screen - AppleMediumMachine Learning EngineerDebug a PyTorch U-Net shape mismatch
This question tests your ability to calculate the output dimensions of convolutional layers in a U-Net architecture and resolve shape mismatches caused by cropping or padding requirements during feature map concatenation.
Coding & AlgorithmsTechnical ScreenPyTorchComputer VisionU-Net - AppleMediumMachine Learning EngineerDescribe recent project experiences
Describe recent project experiences evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipTechnical Screen - AppleMediumMachine Learning EngineerDesign a CPA system for ad bidding
This question tests the ability to design an end-to-end Cost-Per-Acquisition (CPA) bidding system, specifically focusing on how to model conversion probabilities and handle the complexities of delayed feedback and bid pacing.
ML System DesignTechnical ScreenSystem DesignML OpsAdTech - AppleMediumMachine Learning EngineerDesign a multimodal RAG assistant
This question evaluates system design and machine learning engineering skills for building a multimodal Retrieval-Augmented Generation (RAG) assistant, covering competencies in data ingestion and preprocessing across modalities, indexing and retrieval strategies, embeddings and re-ranking, grounding/prompting with citations, and evaluation and failure-mode mitigation. It is commonly asked to gauge an engineer's ability to architect scalable, grounded QA pipelines that balance retrieval quality, latency, and cost; it sits in the System Design domain and tests both high-level architectural reasoning and practical implementation considerations.
System DesignTechnical Screen - AppleHardMachine Learning EngineerDesign a streaming embedding-based classifier
This question evaluates a candidate's ability to design end-to-end streaming machine learning systems, including online text preprocessing, tokenization, embedding generation, continuous model training, and low-latency classification serving.
ML System DesignTechnical Screen - AppleMediumMachine Learning EngineerExplain Vision Encoders and LLM Bottlenecks
This question evaluates understanding of vision encoders and their role in computer vision and multimodal models, familiarity with typical training approaches for encoders, and the ability to identify inference bottlenecks in large language models as well as knowledge of memory and latency optimization considerations.
Machine LearningTechnical Screen - AppleHardMachine Learning EngineerExplain annotation agreement and LLM vs human judges
This question tests a machine learning engineer's understanding of annotation reliability metrics and the trade-offs between human and LLM-based evaluation. It assesses conceptual and applied knowledge of inter-annotator agreement, metric selection across label types, and the validity of automated judges in model evaluation workflows.
Machine LearningTechnical Screen - AppleMediumMachine Learning EngineerFind Minimum Processing Rate
This problem tests your ability to use binary search on a search space to find an optimal minimum processing rate that satisfies a global deadline constraint.
Coding & AlgorithmsTechnical ScreenBinary SearchGreedyOptimization - AppleMediumMachine Learning EngineerHow do you align ambiguous cross-functional projects?
This question evaluates communication, cross-functional collaboration, stakeholder management, project scoping under ambiguity, product-technical integration, and domain-specific experience with AI-agent-based systems and designer collaboration.
Behavioral & LeadershipTechnical Screen - AppleEasyMachine Learning EngineerImplement Masked Multi-Head Self-Attention
This question tests the ability to implement the core mechanics of a Transformer's multi-head attention mechanism, specifically focusing on linear projections, attention weight scaling, and causal masking.
Machine LearningTechnical ScreenTransformersDeep LearningAttention - AppleMediumMachine Learning EngineerImplement TF-IDF scoring for documents
This question evaluates understanding of information retrieval and text representation concepts—specifically TF–IDF weighting, tokenization, term frequency and document frequency—and measures competency in implementing an efficient scoring and ranking algorithm.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerImplement bag-of-words similarity search from scratch
This question evaluates knowledge and implementation skills in text processing, vector-space models, and information retrieval, covering tokenization, term-frequency and TF–IDF weighting, cosine similarity, and ranking for similarity search within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerRotate a Matrix In Place
This question evaluates proficiency in in-place matrix and array manipulation, spatial data transformation, and reasoning about time and space complexity.
Coding & AlgorithmsTechnical Screen - AppleHardMachine Learning EngineerWalk Through an Agentic PDF Evaluation Codebase
This question tests your ability to navigate, critique, and extend a modular codebase designed for evaluating agentic PDF processing pipelines, focusing on evaluation metrics and robust system architecture.
Software Engineering FundamentalsTechnical ScreenAgentic-WorkflowsEvaluationLLM-Systems
Adobe
Airbnb
Amazon
Anthropic
Apple