Eugene Yan Portfolio
Member of Technical Staff at Anthropic (previously led ML/RecSys teams at Amazon, Alibaba, Lazada) whose site hosts 200+ technical essays, working prototypes, and open-source resources built from real production ML experience.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Curated open-source resources drawn from real industry practice
His curated collections synthesize how real companies run ML in production, showing an ability to abstract lessons across companies.
Working prototypes alongside essays
Shipping runnable prototypes alongside writing means claims are backed by something a reader can try, not just prose.
Multi-company production ML experience made legible
Shipping ML systems at four different companies and writing candidly about what changed between contexts is a rare, generalizable signal.
What ML Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Turn what you learn on the job into a public, updated resource list — maintaining it over time is itself evidence of engagement.
Ship small working prototypes alongside your writing.
When you have worked at multiple companies, write comparative essays about what differed in how ML was run.
Publish consistently over years rather than in a single portfolio sprint.
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