Ask HN What to learn for AI Researcher interview? (github.com)

A recent discussion on the Hacker News forum explored the key topics and skills necessary for aspiring junior AI researchers preparing for interviews at leading AI labs. The focus included challenging areas such as efficiently training large language models with limited memory, optimizing inference across distributed systems, and writing CUDA kernels. The proposed curriculum encompasses essential domains like mathematics, coding, AI training, and GPU programming, structured to enhance practical understanding through articles, hands-on code examples, and curated further readings. This initiative is significant for the AI/ML community as it addresses a critical gap in AI education, equipping future researchers with a comprehensive toolkit to handle modern AI challenges. Each curriculum module connects practical tasks to theoretical concepts, ensuring that candidates develop a robust understanding of foundational principles alongside implemented algorithms. By fostering skills in areas such as memory management, debugging, distributed inference, and alignment in AI systems, the program collectively strengthens the skillset needed to innovate in the rapidly evolving field of artificial intelligence.