OpenAI Interview Questions
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Anthropic Behavioral: AI Safety Views for Engineers
Practice Anthropic behavioral AI safety questions: learn what to highlight, how to connect safety frameworks to real work, and actionable examples to discuss.
Anthropic Coding Interview: Domain-Scoped Web Crawler
Implement a domain-scoped web crawler for Anthropic's coding interview: build single-threaded, multi-threaded and asyncio variants with politeness and dedup. Practice it.
Anthropic ML Coding: Prompt-based Binary Classifier
Build a prompt-based binary classifier from per-token log-probs, convert scores to P_pos, compute accuracy & cross-entropy without libraries. Read steps & tips.
Anthropic ML System Design: Scalable Batch Inference
Design a scalable batch inference system for high-volume ML at Anthropic. Learn dynamic batching, GPU autoscaling, reliability, and observability—prepare diagrams and metrics.
Anthropic System Design: Stateless Prompt Playground
Design a stateless prompt engineering playground backend for Anthropic: handle 10MB+ prompts, multi-window sharing, streaming LLM calls, and cost/security trade-offs.
Debug and Extend GPT-style Transformer — OpenAI ML Engineer
Fix 4 intentional bugs in a PyTorch GPT-style transformer, add KV-cache and a token classifier, and reproduce reference training outputs. Learn verification steps.
Google ML Coding: Hand-code Multi-Head Attention in NumPy
Implement multi-head attention in NumPy: scaled dot-product for batched Q,K,V. Do per-head projections, reshape, apply mask, and return attention weights.
OpenAI Coding Interview: Time-Based GPU Credit System
Replayable time-based GPU credit system for OpenAI interviews. Covers event-sourced adds/charges, expiry rules, persistence and out-of-order timestamps.
OpenAI ML Coding: Noisy Human-Labeled Text Classifier
Analyze noisy human annotations and train embedding-based classifiers for identity_attack labels. Filter reliable annotators, retrain models, and propose robustness steps. Start preparing.
OpenAI ML System Design: Scalable Enterprise RAG
Prepare to design a scalable enterprise RAG system for document Q&A and customer support. Review architecture, retrieval, security, and deployment tips for OpenAI ML interviews.
Type Signature Serialization & Inference — OpenAI
Design deterministic type-signature serialization, implement Node/Function types, and infer named type variables to concrete types. Read examples and edge cases.
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