Sebastian Raschka Portfolio
LLM research engineer (Lightning AI) and bestselling author ("Machine Learning with PyTorch and Scikit-Learn," "Build a Large Language Model From Scratch"); his site links directly to from-scratch LLM implementations and a newsletter read by 200k+ practitioners.
What makes it work
A breakdown of the choices that make this portfolio stand out.
"From scratch" implementations as proof of fundamentals
Building an LLM from scratch in code proves understanding of attention, tokenization, and training loops at a level using a library alone cannot demonstrate.
Publishing at book length, repeatedly
Authoring multiple technical books that become industry references requires sustaining accuracy and clarity over hundreds of pages.
A large, engaged newsletter as a distribution and feedback channel
A 200k+ reader newsletter means claims get checked by a large expert audience in near real time.
What ML Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Build one major concept "from scratch" without high-level library shortcuts — the highest-signal project type for proving fundamentals.
Write long-form technical content consistently rather than one-off posts.
Consider longer-form writing (a book, a multi-part guide) once you have enough practical experience.
Link your code, writing, and talks from one central homepage.
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