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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 6271–6300 of 9403 results
- OpenAIHardSoftware EngineerDesign a scalable payment system
Design a scalable payment system 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 - 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 - OpenAIMediumSoftware EngineerDesign a social network with snapshots
This problem tests your ability to design a memory-efficient versioned graph structure that supports efficient edge retrieval at arbitrary points in time.
Software Engineering FundamentalsTechnical ScreenGraphData StructuresVersioning - OpenAIHardSoftware EngineerDesign a team chat system
This question tests the ability to design a scalable, low-latency, real-time messaging system, focusing on message ordering, delivery guarantees, and high-concurrency state management.
System DesignTechnical ScreenScalabilityWebSocketsDistributed Systems - OpenAIMediumSoftware EngineerDesign a text-to-video GPU job scheduler with one GPU per task and fair queueing
Design a GPU job scheduler for a text-to-video generation service where each task runs on a single GPU and users share capacity through fair queueing. It tests job store design, fairness by GPU time rather than task count, leases and retries, and failure handling across workers, GPUs and the scheduler.
System DesignOnsite - OpenAIHardSoftware EngineerDesign an AI playground editor
This problem tests your ability to design a scalable, low-latency document editor that integrates real-time AI streaming responses into a persistent, collaborative text environment.
System DesignTechnical ScreenReal-timeStreamingAPI Design - OpenAIHardMachine Learning EngineerDesign an AWS fine-tuning platform for LLMs
This question evaluates a candidate's competency in designing cloud-native ML platforms for fine-tuning large language models, covering distributed training, multi-tenant security and isolation, cost controls, reproducibility, observability, checkpointing, and deployment workflows.
ML System DesignOnsite - OpenAIMediumMachine Learning EngineerDesign an Agent Harness and Evaluation System
Design a safe harness and evaluation platform for a tool-using AI agent. Address isolated execution, repeatable task sets, trajectory tracing, deterministic and rubric-based graders, repeated trials, privacy, regression gates, and reward-hacking risks.
ML System DesignOnsite - OpenAIMediumSoftware EngineerDesign an Asynchronous Text-to-Video Generation Service
This question tests the ability to design a high-latency, asynchronous job-processing pipeline with a focus on fault tolerance, job state management, and reliable resource handling.
System DesignOnsiteAsync ProcessingScalabilityJob Queues - OpenAIMediumMachine Learning EngineerDesign an Editable Text Buffer
This problem tests your ability to design an efficient data structure for text manipulation while implementing undo/redo history and a dynamic prefix-based autocomplete feature.
Coding & AlgorithmsTechnical ScreenData StructuresTrieUndo-Redo - OpenAIHardMachine Learning EngineerDesign an Extensible Simulation Engine
This problem tests object-oriented design patterns and modular architecture by requiring the implementation of a simulation engine that remains agnostic to the specific rules of a two-player turn-based game.
Software Engineering FundamentalsOnsiteSystem DesignObject-Oriented DesignDesign Patterns - OpenAIHardMachine Learning EngineerDesign an In-Memory Key-Value Cache
Implement a thread-safe in-memory key-value cache supporting capacity bounds, LRU eviction, and per-key time-to-live (TTL) expiration.
Software Engineering FundamentalsTechnical ScreenData StructuresConcurrencyLRU Cache - OpenAIMediumSoftware EngineerDesign an Instagram-like Feed System
This question assesses the ability to architect a high-scale feed system by balancing pre-computation (push model) versus on-demand computation (pull model) to minimize latency for social media users.
System DesignTechnical ScreenScalabilityCachingData Modeling - OpenAIHardMachine Learning EngineerDesign an ML search system
This question evaluates system-design and information-retrieval competencies for machine-learning-powered enterprise document search, covering ingestion and indexing pipelines, hybrid lexical and semantic retrieval, feature generation and ranking, multi-tenant access control, and security/compliance.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign an ML search system with RAG
Design an ML search system with RAG evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - OpenAIMediumMachine Learning EngineerDesign an OOD detection system
This question tests the ability to design an end-to-end system for detecting out-of-distribution (OOD) data in production LLMs to ensure reliability and safety.
ML System DesignTechnical ScreenLLMMLOpsOOD - OpenAIMediumSoftware EngineerDesign an Offline Merchant Payment System with Reconciliation
This question tests your ability to design a distributed payment architecture that supports asynchronous transactions and reliable reconciliation between offline Point-of-Sale (POS) terminals and a centralized ledger.
System DesignTechnical ScreenDistributed SystemsEventual ConsistencyPayment Gateway - OpenAIHardSoftware EngineerDesign an On-Demand Cloud Notebook Service for ML Engineers
Design a platform that gives ML engineers on-demand, GPU-backed notebook workspaces they can create, connect to, pause, and resume, covering lifecycle and API design, provisioning latency, persistent storage, and idle-cost control.
System DesignTechnical Screensystem-designinfrastructurescalability - OpenAIHardSoftware EngineerDesign an Online Chess Platform: Elo Matchmaking, WebSocket Moves, Game Clocks
Design an online chess platform: match players of similar Elo rating quickly, deliver moves in real time over WebSocket connections, and enforce fair game clocks despite network latency and disconnections. It tests matchmaking, stateful real-time architecture, server-authoritative timing and failover.
System DesignOnsite - OpenAIMediumSoftware EngineerDesign an Online Chess Service from Launch to Scale
This problem tests the ability to design a real-time, stateful gaming system, focusing on concurrency control, low-latency communication, and fault tolerance.
System DesignOnsiteReal-timeConcurrencyWebSockets - OpenAIHardSoftware EngineerDesign an Online Devbox Service for On-Demand Cloud Development Environments
System design question about an online devbox service that gives engineers cloud development environments they can create, connect to, stop and resume. It tests lifecycle and API design, fast provisioning, persistent workspace storage, connection routing, isolation of user code, idle cost control and failure handling.
System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign an Online Development Box
Design an online devbox with authenticated workspace access, isolated code execution, persistent project files, reliable stop/resume, and controlled startup costs.
System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign an Online Multiplayer Chess Service with Real-Time Moves
Design an online chess service where two remote players play live games, every move is validated and delivered to the opponent immediately, and results are recorded. It tests authoritative game state, real-time transport, fair clocks, move ordering, recovery of games in progress after failures, and scaling to many concurrent games and spectators.
System DesignOnsite - OpenAIHardSoftware EngineerDesign an SSH-Based Cloud Development Environment
This question tests your ability to design a distributed system that manages stateful, long-running compute resources while ensuring isolation, high availability, and secure remote access.
System DesignOnsiteDistributed SystemsCloud InfrastructureSecurity - OpenAIHardSoftware EngineerDesign an enterprise RAG assistant for internal docs
This question evaluates expertise in designing and training Retrieval-Augmented Generation (RAG) systems, including retriever, evaluator (reranker/verifier/filter), and generator components, with emphasis on model architecture choices, training objectives, data preparation under privacy and document-permission constraints, and evaluation strategies for grounded answers with citations. It is commonly asked to probe advanced ML system design and operationalization skills for mitigating hallucination, stale or conflicting sources, and long-document retrieval; the category is ML System Design and the level is practical application-focused with detailed modeling and training considerations.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign an image/video near-duplicate detection system
This question evaluates competency in ML system design and large-scale multimedia retrieval, focusing on perceptual fingerprinting versus embedding strategies, scalable indexing and nearest-neighbor retrieval, and robustness to resizing, re-encoding, watermarks, minor edits, and adversarial manipulations.
ML System DesignOnsite - OpenAIHardSoftware EngineerDesign an in-memory database
This problem tests the ability to architect a high-performance, in-memory key-value store, focusing on concurrency control, data structures, and durability guarantees.
System DesignTechnical ScreenConcurrencyData StructuresCaching - OpenAIHardSoftware EngineerDesign an in-memory key-value database
Design an in-memory key-value database 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 - OpenAIHardData ScientistDesign and Defend a Free-Trial Conversion Analysis
Walk through how to plan, execute, and defend a free-trial conversion analysis end to end: cohort definition, data-quality checks, standards for a credible insight, and a recommendation deck that survives live pushback.
Analytics & ExperimentationOnsiteexperimentationmetricscase-study - OpenAIHardMachine Learning EngineerDesign and optimize a RAG system
An OpenAI machine learning engineer onsite ML system design question: design, optimize and evaluate a production Retrieval-Augmented Generation (RAG) system that answers questions over a private corpus with citations. The merged answer covers the full pipeline — ingestion and layout-aware parsing, structure-aware chunking, embeddings and versioning, hybrid BM25 plus vector retrieval with RRF fusion, cross-encoder reranking, grounded generation with forced citations, abstention and a fallback ladder, ACL enforcement during retrieval, and the insert/edit/delete freshness lifecycle. It then gives a stage-by-stage evaluation plan (chunk answerability, hit-rate@K, recall@K, nDCG, faithfulness, citation quality, end-to-end success), how to bootstrap labels with no gold set, and follow-ups on debugging a wrongly cited document, embedding-model upgrades, multi-hop versus list-all queries, near-duplicates, conflicting sources and validating an LLM judge.
ML System DesignOnsite
OpenAI