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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
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- 11
- Topics
- 8
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Showing 1–30 of 351 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 - 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 - 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 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 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 - 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 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 - AnthropicHardMachine Learning EngineerDemonstrate culture fit and leadership
This question evaluates interpersonal and leadership competencies for a Machine Learning Engineer role, including cultural adaptability, trust-building, stakeholder alignment, cross-cultural communication, inclusion, delivering and receiving difficult feedback, and validating decisions with guardrails or safety checks.
Behavioral & LeadershipOnsite - AnthropicHardMachine Learning EngineerDeploy a Large Model to GPU Workers
Design a high-throughput, fault-tolerant artifact distribution system to deploy a 500 GB large language model across thousands of GPU worker nodes under strict time and bandwidth constraints.
System DesignOnsiteML InfrastructureDistributed SystemsNetworking - AnthropicHardMachine Learning EngineerDesign a Dynamically Batched Inference API
Design a scalable, multi-tenant LLM inference serving platform that balances Time-to-First-Token (TTFT) and throughput using dynamic iteration-level batching, KV cache memory management, and strict admission control.
System DesignOnsiteLLM ServingDistributed SystemsInference Engine - AnthropicMediumMachine Learning EngineerDesign a model downloader
This question assesses the architectural design of a distributed, secure, and resilient system capable of deploying large-scale machine learning models to heterogeneous edge devices.
ML System DesignOnsiteSystem DesignMLOpsDistributed Systems - AnthropicHardMachine Learning EngineerDesign a scalable service and model performance
This question evaluates a candidate's ability in large-scale distributed system design, performance modeling, capacity planning, and operational reliability for latency-sensitive, multi-region key-value services.
System DesignOnsite - AnthropicHardMachine Learning EngineerDesign an inference routing and scheduling layer
This question evaluates system-design and production machine learning engineering skills, specifically distributed systems, routing and scheduling, multi-tenant isolation, caching, dynamic batching, and capacity planning for heterogeneous GPU/CPU inference backends.
ML System DesignOnsite - AnthropicMediumMachine Learning EngineerExplain batch inference design
This question tests your ability to design a robust, production-grade batch inference pipeline, focusing on data consistency, system reliability, and scalability.
Machine LearningOnsiteSystem DesignMLOpsBatch Processing - AnthropicHardMachine Learning EngineerExplain management style, execution strategy, and culture choices
This question evaluates leadership, people-management, execution strategy, and product-oriented decision-making within a machine learning engineering context, covering conflict resolution, low-performer handling, fair calibration, strategy for ambiguous ML products, retrospectives, and culture-fit reasoning.
Behavioral & LeadershipOnsite - AnthropicMediumMachine Learning EngineerExplain multithreading vs multiprocessing
This question tests your conceptual understanding of how process-based and thread-based concurrency models leverage hardware resources and their respective trade-offs in the context of Python and high-performance ML workloads.
Software Engineering FundamentalsOnsiteConcurrencyPythonOperating Systems - AnthropicMediumMachine Learning EngineerFill In the Sampling and Decode Loop of an LLM Inference Notebook
Anthropic Fellows final-round practice: complete temperature/top-k/top-p sampling and a batched KV-cache decode loop in a Colab notebook so greedy output matches model.generate.
Machine LearningOnsiteAnthropic FellowsAI Safety Fellowshipllm-inference
Adobe
Airbnb
Amazon
Anthropic