AI Researcher Interview Preparation Course Repository Released
Understand this story
AI Research Engineer skrushev has released a learning course repository, AiPrepLab, aimed at preparing for entry-level AI researcher interviews at major tech companies. The motivation was a friend asking what one needs to learn to pass such interviews. The course covers four major areas—mathematics, programming, AI training, and GPU programming—across 11 modules. According to the repository description, almost all content can run on an ordinary CPU without downloading models; the only exception is the CUDA module, which requires a GPU to explore low-level programming. A GPU is helpful for modules 05 and 09, and is required for the CUDA portion of module 09, but half of that module's notebooks do not require a GPU.
AI overview based on source reports · Updated 7 hr ago
Coverage timeline
Follow the coverage to explore different perspectives.
- Hacker News AIAsk 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.
Activity for this story
- Current comparable activity
- 8
- Peak comparable activity
- 10Nov 8 07:00
- Change in the past 24 hours
- –
Trends compare the same fully observed participants, which may cover fewer participants than current activity. Move the pointer or tap the chart to inspect hourly activity; use the left and right arrow keys with a keyboard.