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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 1–30 of 31 results
- 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 - 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 - 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 - 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 - 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 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 - 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 - Capital OneMediumMachine Learning EngineerValidate virtual credit card transactions from encoded IDs
This question evaluates understanding of virtual credit card product trade-offs and the ability to design data models and deterministic validation logic for encoded transaction attributes, testing competencies in payment systems, data encoding/decoding, and rule-based validation.
System DesignOnsite - DoorDashMediumMachine Learning EngineerDesign a scalable recommendation serving system
This question evaluates an engineer's ability to design scalable, low-latency, and resilient online recommendation serving infrastructure, emphasizing caching strategies, partitioning/sharding, data storage consistency, and observability.
System DesignOnsite - DoorDashMediumMachine Learning EngineerDesign personalized restaurant search and recommendations
This question tests the ability to design an end-to-end personalized restaurant recommendation system, covering feature engineering, retrieval-ranking architecture, and infrastructure trade-offs.
System DesignTechnical ScreenRecommendation SystemsRankingSearch - GoogleHardMachine Learning EngineerDesign large-scale near-duplicate video detection
This question tests the ability to design an end-to-end pipeline for detecting near-duplicate videos at a massive scale, focusing on embedding generation, vector indexing, and trade-offs between precision and recall.
System DesignOnsiteComputer VisionEmbeddingsANN Search - J.P. MorganMediumMachine Learning EngineerDesign an Entitlement-Aware Agentic Portfolio Workflow
This problem tests the ability to architect an LLM-based agentic system that balances autonomous task execution with strict role-based access control (RBAC) and auditability.
System DesignTechnical ScreenAgentic WorkflowSecurityLLM - Jane StreetHardMachine Learning EngineerDesign and implement a tiny language runtime
Design and implement a tiny language runtime 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 - Jane StreetHardMachine Learning EngineerImplement compiler for custom language
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Implement compiler for custom language states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - LinkedInHardMachine Learning EngineerDesign a distributed key-value store
Design a distributed key-value store 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 - MercorMediumMachine Learning EngineerDesign a DAG Pipeline Runner for Data-Labeling Jobs
Design a multi-tenant service that runs user-defined DAGs of automated steps (pre-processing, model pre-labeling, QA checks, exports), tracks every run, and survives crashes. Tests data modeling, separating the scheduler from workers, durable state, retries and idempotency, tenant fairness and observability.
System DesignOnsiteworkflow-orchestrationdistributed-systemsstate-machines - MercorMediumMachine Learning EngineerDesign a High-Demand Event Ticket Marketplace
Design an event ticketing platform capable of handling flash-sale spikes without overselling, featuring transactional seat holds, idempotent payment processing, and virtual queueing.
System DesignOnsitesystem-designdistributed-systemsconcurrency - MercorEasyMachine Learning EngineerDesign a Social Feed
Design an end-to-end personalized social feed system capable of serving low-latency ranked content while handling hybrid fanout, multi-stage ML candidate retrieval, and stable pagination.
System DesignTechnical Screensystem-designfeed-rankingrecommendation-systems - MercorMediumMachine Learning EngineerDesign a workflow orchestration system that runs multi-step jobs reliably
A system design question asking you to build a workflow system where users define multi-step workflows with dependencies, start runs, and track progress. It tests data modeling, orchestration and worker architecture, durable state, retries and idempotency, fairness between tenants, scaling and observability.
System DesignOnsite - MetaHardMachine Learning EngineerDesign a recommendation system
This question evaluates competence in designing large-scale recommendation systems and ML engineering skills such as candidate generation, feature store design (offline/online and point-in-time correctness), real-time signal ingestion, ranking and re-ranking, online exploration, cold-start strategies, feedback-bias mitigation, experimentation, and operational reliability. It is commonly asked to assess the ability to make architecture-level trade-offs for scalability, latency and throughput targets, multi-objective optimization, data governance and outage fallback behavior; it falls under the System Design and Machine Learning domain and requires practical, architecture-level application with conceptual trade-off reasoning rather than low-level implementation detail.
System DesignOnsite - MetaMediumMachine Learning EngineerDesign a weapon-ad harmful content detection system
This question assesses the ability to architect a scalable, multi-modal machine learning pipeline for detecting policy-violating content (weapon advertisements) within a high-throughput ad-delivery system.
System DesignTechnical ScreenMulti-modal MLComputer VisionNLP - MetaHardMachine Learning EngineerDesign scalable media storage and delivery
This question evaluates an engineer's ability to design globally distributed, highly scalable media storage and delivery systems, covering competencies in object storage and metadata modeling, CDN integration, replication and consistency strategies, security and access control, background processing, observability, quotas, and cost/performance trade-offs. It is commonly asked in system design interviews to assess reasoning about durability, availability, latency and cost trade-offs while requiring both high-level architectural thinking and practical implementation details; category: System Design; level of abstraction: combines conceptual architecture with practical application considerations.
System DesignOnsite - MicrosoftMediumMachine Learning EngineerDesign chat and online chess
This problem tests your ability to design a distributed system that supports low-latency, real-time interactions for both chat messaging and synchronized game state for a chess platform.
System DesignOnsiteWebSocketsScalabilityConcurrency - MolocoadsMediumMachine Learning EngineerCompute the Longest Run of Ones Across Multiple Machines
This problem tests the ability to design a distributed data reduction pattern where partial results can be combined using associative operators to compute a global property.
System DesignOnsiteDistributed SystemsAlgorithmsMapReduce - OpenAIMediumMachine Learning EngineerDesign Duplicate File Detection
This question tests the ability to design a scalable content-based deduplication system using cryptographic hashing and distributed storage principles to manage large-scale file ingestion.
System DesignOnsiteScalabilityDistributed SystemsHashing - OpenAIMediumMachine Learning EngineerDesign Real-Time Collaborative Editing
This question tests your ability to design a low-latency, distributed synchronization system that manages concurrent text edits from multiple users while maintaining eventual consistency.
System DesignTechnical ScreenDistributed SystemsCRDTConcurrency - OpenAIHardMachine Learning EngineerDesign a regional surge pricing strategy
This question evaluates skills in designing real-time surge pricing systems, assessing competencies in machine learning modeling, control and pricing logic, data engineering, operational robustness, explainability, and monitoring within the System Design domain.
System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a search query autocomplete system
This question evaluates a candidate's ability to design a real-time, low-latency search autocomplete system, exercising competencies in architecture, indexing and serving, ranking and personalization, typo-tolerance, multilingual support, abuse prevention and privacy-aware data handling; it is categorized under System Design and emphasizes practical application with architectural-level conceptual reasoning. It is commonly asked to assess how candidates balance stringent latency and scalability requirements with personalization and trending signals, robustness to input errors and multiple languages, and metrics-driven trade-offs across caching, ranking and monitoring.
System DesignOnsite - SnapchatMediumMachine Learning EngineerDesign a search-to-ads ranking pipeline
This question tests the ability to design a high-throughput, low-latency ranking system that balances user experience with monetization goals through multi-stage retrieval and bidding mechanics.
System DesignOnsiteRankingRecommendation-SystemsAd-Tech
Anthropic
Apple
Atlassian
Capital One
DoorDash
Google
J.P. Morgan
Jane Street
LinkedIn
Mercor
Meta
Microsoft
OpenAI
Snapchat