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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.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 6241–6270 of 9403 results
- OpenAIHardSoftware EngineerDesign a Text-to-Video Generation Platform (Sora-style)
This problem evaluates your ability to design a distributed ML infrastructure that handles high-latency, resource-intensive inference jobs while ensuring scalability, reliability, and content safety.
ML System DesignOnsiteDistributed SystemsML InfrastructureTask Queues - OpenAIMediumSoftware EngineerDesign a Text-to-Video Generation Service
This question tests the ability to design a scalable, reliable asynchronous inference pipeline for resource-intensive generative models, focusing on task orchestration, GPU scheduling, and latency management.
ML System DesignTechnical ScreenML-SystemsDistributed-SystemsGPU-Orchestration - OpenAIHardSoftware EngineerDesign a Text-to-Video Generation System
This question evaluates a candidate's competency in designing scalable ML inference platforms, including GPU resource management and scheduling, asynchronous job orchestration and state-machine-driven job lifecycles, failure classification and recovery, artifact storage, rate limiting, and monitoring for quality and latency.
ML System DesignOnsite - OpenAIMediumSoftware EngineerDesign a Tier-Aware AI Video Generation Service
This problem tests your ability to design a distributed, asynchronous task processing system that balances resource fairness, reliability in the face of compute failures, and tiered prioritization.
System DesignTechnical ScreenDistributed SystemsQueuesTask Scheduling - OpenAIMediumSoftware EngineerDesign a Time-Windowed GPU Credit Ledger
Design an in-memory credit ledger that manages time-windowed resource grants and consumptions with out-of-order event ingestion, deterministic allocation, and balance reconciliation.
Software Engineering FundamentalsTechnical ScreenData StructuresSimulationIntervals - OpenAIMediumSoftware EngineerDesign a URL Shortener
This question tests your ability to design a highly scalable, distributed URL shortening service, focusing on database schema design, request routing, and efficient short-code generation.
System DesignOnsiteScalabilityCachingDatabase Sharding - OpenAIHardSoftware EngineerDesign a URL shortening service
This problem tests your ability to design a distributed, high-scale URL shortening service, focusing on data modeling, collision-resistant ID generation, and performance optimization via caching.
System DesignOnsiteDistributed SystemsDatabase ShardingCaching - OpenAIHardSoftware EngineerDesign a User-Respecting Smart-Grid Controller
Design a distributed demand-response and curtailment controller for an electrical grid that sheds load during emergencies while strictly respecting user preferences, device criticalities, and fairness constraints.
System DesignTechnical Screensystem-designdistributed-systemsiot - OpenAIMediumSoftware EngineerDesign a Video Generation Service with GPU Fleet Management
Tests the ability to design an end-to-end distributed ML inference system for compute-intensive video generation, focusing on GPU scheduling, job lifecycle management, fault tolerance, and cost efficiency.
ML System DesignTechnical ScreenGPU SchedulingDistributed SystemsFault Tolerance - OpenAIMediumSoftware EngineerDesign a chatbot (system design)
This question evaluates system design skills for ML-powered chatbots with a front-end focus, testing understanding of client-side privacy and storage lifetimes, streamed response delivery, stateless relay architectures, authentication boundaries, rate limiting, and failure-mode handling.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign a chatbot fallback for unknown questions
This question evaluates a candidate's competency in ML system design, focusing on handling model uncertainty, retrieval-augmented components, tool integration, decision policies, and operational evaluation within the ML System Design domain.
ML System DesignOnsite - OpenAIHardSoftware EngineerDesign a crossword puzzle solver system
This problem tests the ability to design an efficient search and constraint-solving system for crossword puzzles, requiring optimized data structures for prefix lookups and a backtracking strategy to handle search space pruning.
System DesignTechnical ScreenBacktrackingTrieConstraint Satisfaction - OpenAIMediumSoftware EngineerDesign a distributed crossword fill solver
This problem tests your ability to design a scalable, distributed architecture capable of solving large-scale constraint satisfaction problems (crossword filling) across a vast vocabulary.
System DesignTechnical ScreenDistributed SystemsConstraint SatisfactionBacktracking - OpenAIHardData ScientistDesign a free-month experiment
This question evaluates experimental design, causal inference, metric selection, bias mitigation, ROI estimation, and stakeholder communication skills within the Analytics & Experimentation domain for a Data Scientist role.
Analytics & ExperimentationTechnical Screen - OpenAIHardSoftware EngineerDesign a fullstack app with frontend focus
Design a fullstack app with frontend focus 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 harmful video content moderation system
This question evaluates a candidate's competency in designing scalable, multi-modal machine learning systems for content moderation, covering architecture, model strategy, labeling and human-in-the-loop workflows, metrics, auditability, and defenses against adversarial evasion.
ML System DesignOnsite - OpenAIHardMachine Learning EngineerDesign a low-latency RAG system
This question evaluates ML system design skills, specifically the ability to architect retrieval-augmented generation pipelines under strict latency, cost, and multi-tenancy constraints. It tests how candidates reason about vector search, re-ranking, and LLM integration at production scale — a core competency for senior machine learning engineering roles.
ML System DesignTechnical Screen - OpenAIMediumSoftware EngineerDesign a metrics monitoring system, going deep on one or two components
A system design question about a metrics monitoring system that collects time series from services and hosts, stores them, serves dashboard queries and raises alerts. After a brief end-to-end sketch, the candidate goes deep on one or two areas, such as time-series storage, label cardinality, downsampling or reliable alert evaluation.
System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign a minimal ChatGPT with presets
This question evaluates a candidate's competency in designing multi-tenant conversational AI services with reusable, versioned presets, covering API design, data modeling, session state and context-window management, scalability, multi-tenant isolation, privacy, and observability.
System DesignTechnical Screen - OpenAIMediumSoftware EngineerDesign a multi-device calendar application
This question tests the ability to design a distributed calendar system focusing on synchronization, conflict resolution for overlapping time slots, and efficient storage of recurring events.
System DesignTechnical ScreenDistributed SystemsData ModelingSync - OpenAIHardSoftware EngineerDesign a multi-tenant CI/CD platform
An OpenAI software-engineering system-design screen asking you to design a scalable, multi-tenant CI/CD platform. It spans Git/event intake, YAML workflow parsing into a job DAG, fair scheduling and runner isolation, live log and status streaming, per-tenant quotas and billing, observability, audit logging, disaster recovery, and zero-downtime deploys.
System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign a multi-tenant Slack-like messenger
This question evaluates skills in large-scale distributed systems architecture, real-time messaging and presence, data modeling for multi-tenant platforms, security and compliance features, and operational concerns like observability and cost efficiency.
System DesignTechnical Screen - OpenAIMediumSoftware EngineerDesign a parser for a hypothetical language
This question evaluates competency in compiler and language design, covering grammar analysis, parsing algorithms (LL/LR/GLR/Earley/PEG), lexer and parser implementation, AST construction, error reporting and recovery, operator precedence and associativity, and analysis of time/space trade-offs.
Coding & AlgorithmsOnsite - OpenAIHardSoftware EngineerDesign a payment system with holds and batching
This question tests the ability to design a high-consistency payment processing system that handles asynchronous state transitions, authorization holds, and batch settlement workflows.
System DesignTechnical ScreenPaymentsIdempotencyDistributed Systems - OpenAIMediumSoftware EngineerDesign a persistent key-value store
This question evaluates a candidate's ability to design persistent storage and in-memory data structures, covering serialization of arbitrary values, medium/interface design for byte-level persistence, crash-consistent flushing, error handling, and performance and complexity analysis.
Coding & AlgorithmsTechnical Screen - OpenAIHardMachine Learning EngineerDesign a production RAG system
An OpenAI ML system design interview question (MLE technical screen): design a production, multi-tenant retrieval-augmented generation (RAG) system for enterprise document QA. The model answer covers ingestion and chunking, embedding selection, ANN/vector indexing (HNSW vs IVF-PQ), hybrid retrieval and cross-encoder re-ranking, prompt orchestration with citation grounding, hallucination mitigation, caching and freshness, multilingual and PII/safety handling, scalability and disaster recovery, observability and offline/online evaluation, an API and data schema, cost controls, and a phased rollout. It tests trade-off reasoning across recall, latency, cost, and compliance.
ML System DesignTechnical Screen - OpenAIHardMachine Learning EngineerDesign a recommendation system end-to-end
This question evaluates a candidate's competency in large-scale machine learning system design, including architecture for candidate generation and ranking, feature engineering across real-time and batch pipelines, training and serving workflows, and monitoring for fairness and safety.
ML System DesignOnsite - 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 - OpenAIHardSoftware EngineerDesign a response-ranking ML system
Design a response-ranking ML system 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 - OpenAIEasySoftware EngineerDesign a sandboxed cloud IDE
This question evaluates a candidate's competency in designing multi-tenant distributed systems with strong isolation and sandboxing, runtime lifecycle management, real-time output/log streaming, data persistence, autoscaling, and observability.
System DesignOnsite
OpenAI