Research Engineer 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.
Apple ML Interview: Neural Network Architectures Guide
Apple ML interview: Neural Network Architectures — CNNs, Transformers, attention math, and efficiency optimizations. Get practice tips and examples. Start now.
Apple ML System Design: Multi-modal RAG for Image+Text
Design a low-latency, scalable multi-modal RAG system for hybrid image+text queries. Learn architecture, embeddings, retrieval, LLM synthesis and 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.
From-Scratch PyTorch Transformer — Apple Interview
Implement a runnable, from-scratch PyTorch Transformer (encoder–decoder) with Multi-Head Attention, masks, and residuals. Read steps, tips, and follow-ups.
Google Coding Question: Task Scheduling with Precedence
Minimal makespan for DAG tasks with durations and precedence constraints on unlimited or M CPUs. Includes algorithms, complexity notes, and follow-ups.
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.
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