Ask HN What to learn for AI Researcher interview?

A post describes an interview-preparation curriculum for junior AI researchers at large labs, organized into eleven modules covering PyTorch fundamentals, theoretical foundations, implementation from scratch, ML debugging, system design, inference optimization, RAG, paper analysis, safety and alignment, CUDA, and mixture of experts. Each module pairs an article with a hands-on exercise notebook and reference solutions, with scaled dot-product attention revisited at every level, and most material runs on CPU except the CUDA module. It also suggests time-boxed study budgets and module orderings for coding, systems, and research interview rounds.