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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 6151–6180 of 9403 results
- OpenAIMediumSoftware EngineerDebug a Machine Learning Pipeline
This question evaluates a candidate's ability to diagnose production machine learning failures, covering competencies in data quality, data and concept drift detection, model versioning and deployment checks, and operational debugging within an MLOps context.
Machine LearningTechnical Screen - OpenAIHardMachine Learning EngineerDebug a Small PyTorch Transformer with Four Bugs, Then Add a KV Cache
Debug a small PyTorch transformer language model that hides four bugs, some of which crash and some of which silently ruin training, then add a key-value cache so generation computes only the newest token each step. It tests causal masking, positional encoding, training-loop order and cached decoding.
Machine LearningOnsite - OpenAIMediumMachine Learning EngineerDebug a Transformer Implementation and Implement a KV Cache for Decoding
Practice ML coding question on Transformer language models: find and fix several deliberately planted bugs in a decoder-only Transformer implementation, then implement key-value caching from scratch for autoregressive generation. Tests attention mechanics, tensor-shape reasoning, causal masking, position handling, and verification against full recomputation.
Machine LearningTechnical Screen - OpenAIHardMachine Learning EngineerDebug a broken Transformer implementation
This question evaluates a candidate's ability to debug and validate a Transformer implementation, focusing on attention masking, parameter initialization, loss alignment, and other implementation-level correctness issues.
Machine LearningOnsite - OpenAIHardSoftware EngineerDebug a failing ML classifier
This question evaluates a candidate's ability to diagnose a poorly generalizing ML classifier, testing practical knowledge of data leakage, feature drift, and model calibration. It assesses applied machine learning debugging skills — including cross-validation design and error analysis — commonly tested in senior engineering and ML roles.
Machine LearningTechnical Screen - OpenAIHardMachine Learning EngineerDebug a transformer training pipeline
An OpenAI ML engineer technical-screen debugging question: given a PyTorch Transformer training pipeline whose loss diverges and validation accuracy stays near chance (plus mask IndexErrors, AMP crashes, and nondeterminism), identify the distinct bugs and fix them. It tests Transformer internals - attention/causal masking, teacher-forcing label shift, positional encodings, ignore_index loss, optimizer and GradScaler order, determinism, and DDP - along with writing unit tests and a structured debugging plan.
Machine LearningTechnical Screen - OpenAIMediumData ScientistDebug and harden trial-assignment Python code
This question evaluates understanding of experiment assignment, data integrity, reliability engineering, concurrency, observability, and privacy in data systems, testing competencies around randomness, idempotency, time semantics, retries, and failure-mode identification.
Data Manipulation (SQL/Python)Technical Screen - OpenAIHardMachine Learning EngineerDebug transformer and train classifier
This question evaluates debugging and practical implementation skills for transformer-based text classification, covering model correctness, training loop integrity, data preprocessing, and evaluation metrics within the Machine Learning / Natural Language Processing domain.
Machine LearningTechnical Screen - OpenAIMediumMachine Learning EngineerDefend a Research Direction and Experiment Design
This question evaluates a candidate's ability to synthesize the state of the art in Machine Learning, defend a research direction, and design rigorous experiments, measuring competencies in literature analysis, methodological justification, experimental design, and technical communication.
Machine LearningOnsite - OpenAIHardMachine Learning EngineerDerive Backpropagation for Matrix-Product Layers
This question evaluates understanding of backpropagation through linear blocks, matrix calculus, and the computation of gradients with respect to individual weight matrices in a product of trainable matrices, including reasoning about tensor shapes and matrix differentials.
Machine LearningTechnical Screen - OpenAIMediumMachine Learning EngineerDerive MLE and Bayesian posterior for Bernoulli
This question evaluates parameter estimation and inferential reasoning for Bernoulli/binomial data, including maximum likelihood estimation and its asymptotic variance, Bayesian updating with a Beta prior and the resulting posterior and posterior predictive probability, as well as interval estimation; it is classified in the Statistics & Math domain. It is commonly asked to assess understanding of frequentist versus Bayesian inference, the behavior and reliability of asymptotic approximations and credible intervals, and the practical implications for predictive probability and uncertainty quantification, testing both conceptual understanding and practical application.
Statistics & MathOnsite - OpenAIMediumMachine Learning EngineerDerive Sharded Matrix Multiplication and Backpropagation
Derive forward and backward computation for column-sharded matrix multiplication across multiple devices. Track every local tensor shape, identify the collective needed for replicated input gradients, and propose tests for shard ordering, scaling, and numerical errors.
Machine LearningOnsite - OpenAIMediumMachine Learning EngineerDescribe handling pressure and present your work
Describe handling pressure and present your work 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 - OpenAIMediumProduct ManagerDescribe relevant PM experience
Prepare OpenAI PM behavioral answers on analytical tools, mobile feature optimization, localization, and experiments. Includes STAR-style examples, metrics, guardrails, and tailoring advice.
Behavioral & LeadershipHR Screen - OpenAIMediumAndroid EngineerDesign Android MVVM API Architecture
Design a robust, scalable Android client architecture using MVVM and Unidirectional Data Flow (UDF) to interact with remote REST and streaming APIs, handling configuration changes, lifecycle events, and coroutine cancellation.
System DesignTechnical ScreenAndroidMVVMKotlin Coroutines - OpenAIMediumSoftware EngineerDesign Bakery Chain Card Payments With Tips and Nightly Settlement
Design the payment system for a bakery chain's counter terminals, where a card is authorized for the purchase, a tip is added afterward, and each location's transactions are captured together in a nightly settlement batch that must scale as the chain grows.
System DesignOnsitesystem-designpaymentsidempotency - OpenAIMediumSoftware EngineerDesign CI/CD Build Caching
This problem evaluates your ability to design a distributed caching system for CI/CD pipelines, focusing on cache key strategies, invalidation, and storage scalability.
System DesignTechnical ScreenDistributed SystemsCI/CDCaching - OpenAIHardSoftware EngineerDesign Command Dispatch and Telemetry Reconciliation for Unreliable Devices
Design a resilient control plane and telemetry reconciliation system for millions of intermittently connected edge devices, ensuring safe command execution, idempotency, and accurate state tracking despite network unreliability.
System DesignTechnical ScreenDistributed SystemsIoTEdge Computing - 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 - OpenAIMediumSoftware EngineerDesign First-Fit and Best-Fit Memory Allocation
Design an in-memory heap allocator supporting first-fit and best-fit allocation, block splitting, adjacent free block coalescing, and safe handle validation to prevent stale or forged free operations.
Software Engineering FundamentalsTechnical ScreenMemory ManagementData StructuresLinked List - OpenAIMediumSoftware EngineerDesign GPU Scheduling for a Video Generation Platform
This question tests your ability to design a distributed job scheduling system that manages heterogeneous GPU resources for computationally expensive, variable-length video generation tasks.
System DesignOnsiteGPU SchedulingDistributed SystemsLatency - OpenAIHardSoftware EngineerDesign GPU credit allocator
A multi-tenant GPU credit allocation system design (OpenAI software-engineer system-design screen): track and deduct GPU credits in real time across many nodes, issue/transfer/spend credits via idempotent APIs, and enforce budgets, hierarchical quotas, rate limits, and fair-share with scheduler integration. Covers prepaid vs postpaid, an immutable double-entry ledger with reservations, overspend prevention, failure recovery, scaling, and observability.
System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign IDE Sandbox and Payments
This question evaluates system design competencies such as distributed systems architecture, scalability, isolation and security, API and state-machine design, storage and synchronization, resiliency, observability, and compliance applied to two domains: a cloud IDE sandbox and a payment processing backend, with domain: System Design.
System DesignOnsite - OpenAIHardMachine Learning EngineerDesign LLM search handling long token inputs
Design an LLM search and RAG system that handles long documents and long user queries. Covers chunking, hierarchical retrieval, query compression, context budgeting, citations, latency, and evaluation.
ML System DesignOnsite - OpenAIMediumAndroid EngineerDesign Mobile Model Usage Quotas
Design an end-to-end quota and rate-limiting system for a mobile AI chat client, focusing on client-side state management, API contract design, and multi-device synchronization.
System DesignTechnical Screensystem-designandroidapi-design - OpenAIHardSoftware EngineerDesign Online Chess Matchmaking
This question tests the ability to design a distributed, low-latency matchmaking system that balances player skill (MMR), wait time, and network latency under high concurrency.
System DesignTechnical ScreenScalabilityConcurrencyDistributed Systems - OpenAIMediumSoftware EngineerDesign Online Chess Matchmaking and Clocks
This question tests your ability to design a real-time game backend, focusing on low-latency state synchronization, fair matchmaking, and reliable clock management in a distributed environment.
System DesignTechnical ScreenReal-timeConcurrencyWebSockets - OpenAIMediumSoftware EngineerDesign Payment Authorization, Capture, and Batch Settlement
Design a resilient payment processing platform supporting two-phase payment operations (authorize and capture), double-entry ledgering, and asynchronous batch settlement with external financial gateways.
System DesignTechnical ScreenPaymentsDistributed SystemsLedger - OpenAIHardSoftware EngineerDesign Photo Storage with SHA-256 Content Deduplication and Safe Deletion
Design a photo storage service that deduplicates identical uploads by their SHA-256 content hash. Cover the upload flow with server-side verification, concurrent identical uploads, deletion with reference counting and garbage collection, and the privacy risks of deduplicating across users. It tests content-addressed storage design.
System DesignOnsite - OpenAIHardSoftware EngineerDesign Power-Grid Device Monitoring over Unreliable Networks: Dedup and Late Events
Design a monitoring platform for power-grid devices that report readings and upload logs over unreliable public networks. Ingestion must be idempotent, deduplicate retransmissions, handle out-of-order and late events and accept batched uploads. It tests IoT ingestion, event-time processing and resilience to reconnect storms.
System DesignOnsite
OpenAI