OpenAI interview questions

Showing 331–342 of 342 results

• Text Editor in Four Stages: Buffer, Undo/Redo, Trie Autocomplete, Collaborative OTHard

  Build a text editor in four stages: a buffer with insert, delete and read, undo and redo with two stacks, trie-based autocomplete ranked by word frequency, and real-time collaborative editing with operational transformation. It tests data structure choice, reversible operations, top-k retrieval and concurrency correctness.

  OpenAISoftware EngineerSoftware Engineering FundamentalsOnsite
• Three-Card Game: Deal Hands, Rank Them, Then Add a Flush CategoryMedium

  A two-part card game coding exercise: deal three cards to each player from one deck, show every hand and pick the winner using three of a kind, pair and high card, then add a flush category between them. It tests hand representation, within-category comparison, tie handling and code that extends cleanly when a rule changes.

  OpenAISoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Toy Language Types: Render Types and Signatures, Infer Generic Return TypesHard

  Model the types of a toy programming language, with primitives, generic type variables and nested tuples, render types and function signatures as strings, then infer a call's concrete return type by binding generics consistently across arguments. It tests recursive data modeling, exact formatting and type inference with clear errors.

  OpenAISoftware EngineerSoftware Engineering FundamentalsOnsite
• Track Expiring GPU CreditsMedium

  Design an in-memory credit ledger to track expiring GPU compute credits, support priority consumption, and manage resource debt.

  OpenAIMachine Learning EngineerCoding & AlgorithmsOnsite
• Train a classifier and analyze datasetHard

  This question evaluates proficiency in end-to-end supervised machine learning workflows—covering data validation, exploratory data analysis, preprocessing, model training and selection, evaluation metrics and calibration, fairness assessments, and reproducibility—and tests competencies in applied machine learning engineering, statistical reasoning, and model governance within the Machine Learning domain. It is commonly asked in technical interviews because it verifies practical implementation skills and judgment for deploying reliable classifiers, aligning metrics to business goals and diagnosing performance and fairness across slices, with emphasis on practical application informed by conceptual understanding.

  OpenAIMachine Learning EngineerMachine LearningTechnical Screen
• Train and analyze a classifierMedium

  This question evaluates proficiency in end-to-end machine learning engineering, including exploratory data analysis, time-aware train/validation/test splitting, model baseline and improvement, class imbalance strategies, leakage-safe hyperparameter tuning, metric computation and calibration, error analysis, reproducible training pipelines with CLI/config/seed control, explainability (feature importance/SHAP and ablations), and documentation of risks, fairness checks and monitoring hooks. It is commonly asked to assess practical ability to manage the full ML lifecycle and data hygiene in production-like scenarios, testing applied Data Manipulation (SQL/Python) and Machine Learning competencies with an emphasis on practical application while also requiring conceptual understanding of evaluation, calibration and fairness.

  OpenAIMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
• Vectorize One-Nearest-Neighbor and Express It as a Neural Forward PassMedium

  Tests vectorized distance computation in array libraries and the mathematical equivalence between nearest-prototype classification and an affine neural network layer.

  OpenAIMachine Learning EngineerMachine LearningTechnical Screen
• Why OpenAI, Your View on AGI and AI Safety, and Why You Are Leaving Your RoleHard

  Behavioral questions asked by both the recruiter and the hiring manager at a frontier AI lab: why you want to join this company specifically, your views on artificial general intelligence and AI safety, and why you are leaving your current role. It tests specificity, considered views on AI risk and a consistent story.

  OpenAISoftware EngineerBehavioral & LeadershipOnsite
• Wildfire Grid: Days Until Quiet with Expiring FirebreaksHard

  Simulate a wildfire spreading through a forest grid to find how many days until it stops spreading, then extend the model with fire-resistant trees and firebreaks that only hold for a limited number of days before the protected tree becomes flammable.

  OpenAISoftware EngineerSoftware Engineering FundamentalsOnsite
• Write SQL for repeat churnHard

  This question evaluates proficiency in event-time analytics and cohort churn metric computation using SQL, focusing on correct identification of first qualifying events and aggregation by experimental variant.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to compute signup and retention liftMedium

  This prompt evaluates SQL proficiency in time-windowed cohort analysis, event deduplication, date arithmetic, and per-variant aggregation for A/B experiments, categorized under Data Manipulation (SQL/Python) and targeted at an intermediate-level Data Scientist role.

  OpenAIData ScientistData Manipulation (SQL/Python)Technical Screen
• eSIM Data Wallet with Expiring Bundles and Late Usage RecordsMedium

  Implement a prepaid data wallet where usage draws from the soonest-expiring bundle first and records arrive out of timestamp order, then add checkpointing so balance queries do not replay the full history. Tests event-sourced thinking, heap-based priority consumption, and cache invalidation.

  OpenAISoftware EngineerSoftware Engineering FundamentalsOnsite