# From Big Tech SWE to AI Infra (Part 2): What I Learned in My First Year
**作者**: Yichi Zhang
**日期**: 2026-10-24T15:39:44.000Z
**来源**: [https://x.com/YichiZ03/status/2114019016386003198](https://x.com/YichiZ03/status/2114019016386003198)
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(I wrote the original Chinese version entirely by hand to keep this as honest and personal as possible.)
This July, I shared my first post about moving from a software engineering role at a big tech company into AI infrastructure. It received much more attention than I expected—even some of my friends came across it in their feeds. Thank you to everyone who read it.
It has now been a full year since I started learning AI infra, and a few months since I joined a great AI infra startup. This feels like a good milestone to share the learning path that worked for me, along with some reflections on working in the field.
If you’re learning AI infra too, or have questions about getting started, I’d love to hear from you in the comments.
## My learning timeline
Here’s the full journey, in chronological order.
May 2025
A little background first. If you followed the daily learning notes I used to post, you might remember that I double-majored in CS and Math. I eventually decided to pursue software engineering and followed the familiar path: recruiting, an internship, a return offer, and a SWE role at the big tech company I had dreamed of joining.
I graduated in May. At that point, my exposure to machine learning consisted of a few undergraduate courses and some related lab experience. My math background probably helped a little, too.
July 2025
I officially started my job in July. But life at a big tech company turned out to be very different from what I had imagined.
For one thing, I found it difficult to take initiative. Most of the time, tasks were assigned from above. Working with the same technologies day after day also left me feeling bored, with little room to create something new.
One night, just three months into the job, I started questioning the path I had laid out for myself. It didn’t seem to lead to the kind of life I wanted. I wanted to do work that was meaningful, challenging, and valuable to humanity.
That naturally led me toward open source and AI—and eventually to SGLang.
October 2025
So, three months into my first job, I started learning the fundamentals of AI infra.
My limited ML background and lab experience helped me get started reasonably quickly. But without a structured curriculum to follow, I spent a long time trying to figure out what was actually worth learning.
I ended up learning a bit of this and a bit of that, documenting the process in my early Xiaohongshu posts. I later hid those posts because I no longer thought they were good enough, and I didn’t want to mislead anyone.
January 2026
By January, I felt I had worked through much of the introductory material and many of the blog posts I could find. But understanding concepts and writing real code were still two very different things.
That was when I opened the SGLang codebase and started studying it in depth.
Honestly, reading the source code exposed me to countless concepts and optimization techniques I had never even heard of. Until then, I had mostly learned about things like parallelism strategies, quantization, and radix caching. Suddenly, I was looking at CPU–GPU overlap in the scheduler, CUDA Graphs, HiCache, and so much more.
These concepts feel familiar now. But for a newcomer without the surrounding context, they were overwhelming.
I also started helping out with small tasks in the SGLang community, gaining experience as I learned.
March–April 2026
By this point, my theoretical foundation was coming together. Those early contributions had also helped me build some trust and get to know people in the SGLang community.
Around this time, 吉他猫LLM, whom many people in the community will know, brought me into the SGLang-Omni project. That was where I started doing substantial development work.
The rest followed from there: keep building, keep learning, gradually gain experience, and eventually find my footing as a newcomer in the industry.
The year since October 2025 has been one of the highest-ROI years of my life. Through all those days and nights of studying, I met many people I deeply respect, and I’m grateful for how generously they shared their knowledge.
This year also made me increasingly aware of how limited any one person’s knowledge is, and how much further there is to go in understanding the technology. In the coming year, I hope to keep approaching the work as an apprentice: stay curious, keep learning, and keep getting better.
That’s why I’m sharing my full learning path openly. I hope it gives us something to discuss and build on together. AI infra is a technically demanding field, but I hope my experience can help others who are considering this path or are already on it.
## Reflections on moving from SWE to AI infra
To be honest, while traditional database infrastructure and AI infrastructure share some similarities, my experience has been that there is still an enormous amount to learn from scratch.
This field is also full of exceptionally capable people. Whenever I start feeling a little too pleased with myself, I’m reminded of how far I still have to go and how much I still need to learn.
That said, I wouldn’t recommend that everyone make the switch.
At my previous job, I could work nine to five, fully disconnect afterward, and spend my evenings playing board games or go hiking in my free time. In AI, where competition is intense and the pace of change is relentless, every minute feels spoken for.
For me, what matters most is the meaning behind the work and the value it can bring to humanity. That gives me a strong drive to take initiative—almost an instinctive sense of purpose.
Whether the trade-off is worthwhile depends on the person. For me, at least, this work makes me happy and gives me a real sense of accomplishment.
Looking back, that covers most of what I wanted to share from this past year. What stands out most is just how quickly the AI era is unfolding.
For those of us born in the 2000s, this may be both the best and the worst time of our lives.
The good part is that we get to witness humanity enter another new era, taking productivity—and even our ability to do research—to another level. A new generation will ride this wave and build extraordinary careers and lives.
But working in the field, I also have to acknowledge that AI as a productivity tool is widening the gap between people with resources and those without.
People with access to the most advanced models and the harnesses built around them can accomplish what once took a hundred people. Those without that access have to settle for less, and some may not even realize how much AI can improve their productivity.
This is why I’ve always believed open source matters so much.
If tools this powerful remain in the hands of a few dominant companies or the top 0.1%, perhaps Arasaka from Cyberpunk 2077 won’t remain confined to fiction.
Finally, thank you to everyone contributing to open source. I hope more people will join these communities and contribute.
And again, if you have questions, leave a comment. I’d love for us to learn from one another.
## 相关链接
- [Yichi Zhang](https://x.com/YichiZ03)
- [@YichiZ03](https://x.com/YichiZ03)
- [20K](https://x.com/YichiZ03/status/2114019016386003198/analytics)
- [Upgrade to Plus](https://x.com/i/premium_sign_up)
- [11:39 PM · Oct 24, 2026](https://x.com/YichiZ03/status/2114019016386003198)
- [20.6K Views](https://x.com/YichiZ03/status/2114019016386003198/analytics)
- [View quotes](https://x.com/YichiZ03/status/2114019016386003198/quotes)
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*导出时间: 2026/10/25 13:10:50*
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## 中文翻译
# 从大厂软件工程师到 AI Infra(第二篇):我在第一年学到的东西
**作者**: Yichi Zhang
**日期**: 2026-10-24T15:39:44.000Z
**来源**: [https://x.com/YichiZ03/status/2114019016386003198](https://x.com/YichiZ03/status/2114019016386003198)
**阅读**: 20K
**点赞**: 401
**转发**: 37
**回复**: 4
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(我完全用手写的方式完成了原始中文版本,希望尽可能地保持真诚和个人化。)
今年7月,我发布了第一篇关于从大厂软件工程师转向 AI 基础设施的文章。它获得的关注远超我的预期——甚至一些朋友也在他们的信息流中刷到了它。感谢每一位阅读的人。
如今,我学习 AI infra 已整整一年,加入一家优秀的 AI infra 创业公司也有几个月了。这是一个不错的节点,可以分享对我行之有效的学习路径,以及一些在这个领域工作的思考。
如果你也在学习 AI infra,或者对如何入门有疑问,欢迎在评论区和我交流。
## 我的学习时间线
以下是我按时间顺序整理的完整历程。
2025年5月
先交代一点背景。如果你看过我以前发的每日学习笔记,可能记得我是计算机科学与数学双专业。我最终决定走软件工程的方向,沿着一条再熟悉不过的路径:秋招、实习、转正 offer,然后进入了我梦寐以求的大厂担任软件工程师。
我于5月毕业。当时,我对机器学习的接触仅限于几门本科课程和一些相关的实验室经历。我的数学背景大概也帮了一点忙。
2025年7月
我于7月正式入职。但大厂的生活与我想象的截然不同。
一方面,我发现自己很难主动做些什么。大多数时候,任务都是从上面分配下来的。日复一日使用相同的技术也让我感到枯燥,几乎没有创造新东西的空间。
一天晚上,入职才三个月的我,开始质疑自己为自己规划的道路。它似乎无法通向我想要的生活。我想做有意义、有挑战性、对人类有价值的工作。
这自然把我引向了开源和 AI——最终来到了 SGLang。
2025年10月
于是,入职三个月后,我开始学习 AI infra 的基础知识。
我有限的 ML 背景和实验室经历帮我比较快地入了门。但在没有系统化课程可循的情况下,我花了很长时间去摸索到底什么才是值得学的。
结果就是东学一点、西学一点,并在我早期的小红书帖子中记录了这个过程。后来我隐藏了那些帖子,因为我觉得它们已经不够好,不想误导任何人。
2026年1月
到1月,我感觉自己已经啃完了大部分入门材料和能找到的博客文章。但理解概念和写真正的代码仍然是两回事。
就在那时,我打开了 SGLang 的代码库,开始深入研究它。
说实话,阅读源码让我接触到了无数我闻所未闻的概念和优化技术。在那之前,我大致了解并行策略、量化和 radix 缓存之类的东西。突然之间,我面对的是调度器中的 CPU-GPU 重叠、CUDA Graphs、HiCache 等等更多的东西。
现在这些概念对我而言已经很熟悉了。但对于一个缺乏上下文的新人来说,它们一度让人不堪重负。
我也开始帮忙处理 SGLang 社区里的一些小任务,边学边积累经验。
2026年3月–4月
到这个时候,我的理论基础逐渐成型。那些早期的贡献也帮助我建立了一些信任,并结识了 SGLang 社区里的人。
大约在那时,社区里很多人应该都认识的吉他猫LLM,把我拉进了 SGLang-Omni 项目。那是我开始做实质性开发工作的地方。
之后的一切顺理成章:持续构建、持续学习、逐步积累经验,最终作为行业新人站稳了脚跟。
从2025年10月以来的这一年,是我人生中投入产出比最高的一年之一。在那无数个日夜的学习中,我遇到了许多我由衷敬佩的人,我感激他们慷慨地分享知识。
这一年也让我越来越意识到,一个人的知识是多么有限,而在理解技术的路上还有多远要走。在未来的一年里,我希望继续以学徒的心态对待工作:保持好奇、持续学习、不断进步。
所以我选择公开分享我的完整学习路径。希望这能给我们带来一些共同讨论和共同构建的基础。AI infra 是一个技术门槛很高的领域,但我希望我的经历能帮助那些正在考虑这条道路或已经走在上面的人。
## 从 SWE 转向 AI infra 的一些思考
说实话,虽然传统数据库基础设施和 AI 基础设施有一些相似之处,但我的体会是,仍然有海量的东西需要从头学起。
这个领域也聚集着能力极其出色的人。每当我稍微有点沾沾自喜,就会想起自己还有多远的路要走、还有多少要学。
话虽如此,我并不建议每个人都做出这样的转变。
在上一份工作中,我可以朝九晚五,下班后彻底断开工作,晚上玩桌游,空闲时去徒步。而在 AI 领域,竞争激烈、变化不休,每一分钟都似乎被占满了。
对我来说,最重要的是工作背后的意义以及它能为人类带来的价值。这给了我一种强烈的主动性——几乎是一种本能的使命感。
这种取舍是否值得,取决于个人。至少对我而言,这份工作让我快乐,也给我带来实实在在的成就感。
回望过去,这就是我这一年想分享的大部分内容。感受最深的,是这个 AI 时代展开的速度之快。
对于我们这些千禧年后出生的人来说,这也许既是人生中最好的时代,也是最坏的时代。
好的一面是,我们得以见证人类迈入又一个新时代,生产力——甚至我们做研究的能力——被提升到新的高度。新的一代人会乘着这股浪潮,创造非凡的事业和人生。
但身处这个行业,我也必须承认,AI 作为生产力工具,正在扩大有资源者与无资源者之间的差距。
能够使用最先进模型及其配套工具的人,可以完成过去需要一百人才能完成的工作。而没有这种资源的人只能退而求其次,有些人甚至可能没有意识到 AI 能在多大程度上提升他们的生产力。
正因如此,我一直坚信开源意义重大。
如果如此强大的工具只掌握在少数垄断公司或最顶层的 0.1% 手中,也许《赛博朋克2077》里的荒坂公司就不会只停留在虚构作品中。
最后,感谢每一位为开源做贡献的人。希望更多人加入这些社区并做出贡献。
再次说明,如果你有任何疑问,欢迎留言评论。我很乐意我们一起互相学习。
## 相关链接
- [Yichi Zhang](https://x.com/YichiZ03)
- [@YichiZ03](https://x.com/YichiZ03)
- [20K](https://x.com/YichiZ03/status/2114019016386003198/analytics)
- [Upgrade to Plus](https://x.com/i/premium_sign_up)
- [11:39 PM · Oct 24, 2026](https://x.com/YichiZ03/status/2114019016386003198)
- [20.6K Views](https://x.com/YichiZ03/status/2114019016386003198/analytics)
- [View quotes](https://x.com/YichiZ03/status/2114019016386003198/quotes)
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*导出时间: 2026/10/25 13:10:50*