# I looked at 1,680 Anthropic resumes. Here's who they actually hire.
**作者**: seb
**日期**: 2026-06-11T17:03:50.000Z
**来源**: [https://x.com/hiiinternet/status/2065117819948437765](https://x.com/hiiinternet/status/2065117819948437765)
---

## Builders, not researchers.
I pulled every LinkedIn profile that lists Anthropic as a current employer. 5,306 people. Kept the 1,680 who are actually engineers, then looked through 7,986 of their prior-role descriptions for what they did before they got there.
Here are the numbers.
## They grew the org almost overnight.

15 engineers that are still at Anthropic were there before 2021. The org roughly tripled in 2025 (686 hires) and 2026 is on pace to match it (455 through June).
Half the current engineering org has been there under a year. 53% joined in the trailing 12 months. Median tenure: 10 months.
A giant org, built in about 18 months.
## They almost exclusively hire senior engineers

Median experience before Anthropic: 12.2 years. Middle 50% runs 8.8 to 16.5 years.
Only 50 of the 1,680 have under three years of experience. 44% have 13 or more. New-grad hiring is basically nonexistent.
So the median hire has 12 years of experience and has been there 10 months.
## They index heavily on infra not really research

Infrastructure shows up in 40% of backgrounds. Backend, distributed systems, databases, and security each land around 20%. Reinforcement learning, the "RL" in RLHF, shows up in 3.3%.
The typical Anthropic engineer spent the last decade building large-scale production systems at a hyperscaler or an infra-heavy startup.
Self-listed skills say the same thing: Python 585, Java 566, C++ 443, JavaScript 376, SQL 302, Linux 230, Distributed Systems 189, AWS 154. The glamorous model-training work exists. It's just rare.
## The #1 feeder isn't labs it's Google

Everyone assumes Anthropic raids OpenAI and DeepMind. Its biggest pipeline is Google, by a mile. The rival labs are the two small bars in the middle.
Anthropic over-pulls from places known for engineering rigor: Stripe, Databricks, Snowflake, Palantir, Airbnb.
Ever worked at, anywhere in their history: Google 405, Meta 273, Amazon 197, Microsoft 171, Stripe 124, Apple 87, Stanford 68, DeepMind 62, Airbnb 51, OpenAI 48. Half the org (50%) has FAANG on the resume somewhere.
They're also pulling from other labs OpenAI is a top-5 direct feeder, DeepMind top-6. Roughly 94 engineers moved straight from one frontier lab to them.
## The PhD myth.

Only 13.7% hold a doctorate. One in seven.
The median hire is a senior engineer with a bachelor's or a master's, not a research scientist. The lab-full-of-PhDs image is mostly wrong at the engineering level.
Fields of study skew exactly how you'd expect a builder org to: Computer Science 819, then Mathematics 78, Physics 70, Computer Engineering 69. Philosophy cracks the top 20 (13) (safety?).
## Stanford leads hiring heavily

Schools, all-time: Stanford 144, Berkeley 118, MIT 80, CMU 73, Harvard 42, Cambridge 39, UW 36, Waterloo and Cornell 35 each, Oxford 33, Princeton 32. Those top four are a quarter of the org.
## 80% of them share one job title.
"Member of Technical Staff."
A former Instagram CTO, ex-Adept founders, and Stanford faculty are all just "MoTS." The title is flattened on purpose. Seniority and function are invisible by design.
## The one place "juniors" get in.

172 engineers have under six years of experience. 50 have under three. They are not generic new grads. They split into two archetypes, with almost no ordinary mid-level in between.
Look at how they differ from the org. More PhDs (19% vs 13.7%). Triple the rate of product/SWE titles (15% vs 5%). Far less likely to carry a FAANG resume (32% vs 50%).
What they have instead is pedigree that substitutes for years:
- The internship pipeline. 50% list internships at the following: Meta 16, Google 10, DeepMind 6, Microsoft 5, Amazon 5, plus Jane Street, Two Sigma, HRT, Optiver, Nvidia.
- Quant to lab. 9% came through elite trading shops (Jane Street, Two Sigma, Five Rings, HRT, Optiver, Citadel). Young math/CS competition types coming through HFT.
- Alignment fellowships. 6% touched MATS, SERI, Redwood, or ARC. A junior-only on-ramp that barely exists in the senior cohort.
The clean archetype: MIT, IOI silver medal, 2900+ on Codeforces, straight into RL and safety at four years in. They're screened on competition rank and publications instead of tenure.
They also skew more international than the seniors. Junior schools: Berkeley 15, Stanford 14, Cambridge 10, MIT 7, Tsinghua 7, Oxford 6, plus Imperial, NUS, Shanghai Jiao Tong, ETH Zürich.
## So what do you do with this?
If you want to join Anthropic as an engineer, stop writing your resume for a research lab and write it for an infra company. Show systems you actually built and scaled. That's the resume getting hired. Early-career is the only exception, and there the bar is a top internship, a competition rank, or a paper.
If you're hiring against them, your target isn't a PhD or a lab logo. It's a senior builder from a hyperscaler or an infra-reputation shop, that's twelve years deep. Stripe, Databricks, Snowflake, Palantir. Anthropic is already fishing that pool hard.
---- :)
I'm recruiting engineers for startups backed by A16z, Sequoia, YC, Founders fund, Khosla, etc. Pre-seed to Series C, NYC and SF, Mid-Staff Product and AI engineers with salary ranges ranging from $150k - $400k
We'll tell you within a day if there are roles that fit your skills and interests.
Text our iMessage agent → 646-236-3745 and see who wants to talk to you (and how much they'd pay you not just the salary range) → talent.fonzi.ai/home
## 相关链接
- [seb](https://x.com/hiiinternet)
- [@hiiinternet](https://x.com/hiiinternet)
- [117K](https://x.com/hiiinternet/status/2065117819948437765/analytics)
- [talent.fonzi.ai/home](https://talent.fonzi.ai/home)
- [Upgrade to Premium](https://x.com/i/premium_sign_up)
- [1:03 AM · Jun 12, 2026](https://x.com/hiiinternet/status/2065117819948437765)
- [117.8K Views](https://x.com/hiiinternet/status/2065117819948437765/analytics)
- [View quotes](https://x.com/hiiinternet/status/2065117819948437765/quotes)
---
*导出时间: 2026/6/12 12:11:03*
---
## 中文翻译
# 我分析了 1,680 份 Anthropic 的简历。这是他们真正雇佣的人。
**作者**: seb
**日期**: 2026-06-11T17:03:50.000Z
**来源**: [https://x.com/hiiinternet/status/2065117819948437765](https://x.com/hiiinternet/status/2065117819948437765)
---

## 是建造者,而非研究员。
我提取了所有将 Anthropic 列为当前雇主的 LinkedIn 资料。共有 5,306 人。我筛选出了其中 1,680 名真正的工程师,然后查看了他们 7,986 条关于前一份工作的描述,了解他们在进入 Anthropic 之前做了什么。
以下是数据。
## 他们的组织几乎是一夜之间壮大起来的。

有 15 名工程师在 2021 年之前就一直在 Anthropic。该组织在 2025 年大约增长了三倍(雇佣了 686 人),而 2026 年正以此速度匹配这一数字(截至 6 月已有 455 人)。
目前有一半的工程团队入职不到一年。53% 的人在过去的 12 个月内加入。中位任期:10 个月。
一个庞大的组织,在约 18 个月内建成。
## 他们几乎只雇佣高级工程师

在加入 Anthropic 之前的平均经验:12.2 年。中间 50% 的人区间为 8.8 到 16.5 年。
这 1,680 人中只有 50 人的经验少于三年。44% 的人拥有 13 年或更久的经验。应届毕业生的招聘基本不存在。
所以,中位雇佣者拥有 12 年的经验,并且在那里待了 10 个月。
## 他们非常看重基础设施背景,而非研究

40% 的背景中包含基础设施。后端、分布式系统、数据库和安全各占约 20%。强化学习,即 RLHF 中的“RL”,出现在 3.3% 的背景中。
典型的 Anthropic 工程师在过去十年里是在超大规模云服务商或重基础设施的初创公司构建大规模生产系统的。
自我列出的技能也说明了同样的情况:Python 585,Java 566,C++ 443,JavaScript 376,SQL 302,Linux 230,分布式系统 189,AWS 154。光鲜亮丽的模型训练工作确实存在,只是很罕见。
## 第一大输送方不是其他实验室,而是 Google

大家都认为 Anthropic 会挖角 OpenAI 和 DeepMind。但它最大的人才输送渠道是 Google,而且是遥遥领先。那些竞争对手实验室是中间那两个小柱条。
Anthropic 过度地从那些以工程严谨性著称的地方挖人:Stripe、Databricks、Snowflake、Palantir、Airbnb。
曾经在这些地方工作过(在其历史上的任何时期):Google 405 人,Meta 273 人,Amazon 197 人,Microsoft 171 人,Stripe 124 人,Apple 87 人,Stanford 68 人,DeepMind 62 人,Airbnb 51 人,OpenAI 48 人。一半的组织(50%)在简历的某处有 FAANG 的工作经历。
他们也在从其他实验室挖人。OpenAI 是前 5 大直接输送方,DeepMind 是前 6。大约有 94 名工程师直接从一家前沿实验室跳槽到了他们那里。
## 博士神话。

只有 13.7% 的人拥有博士学位。七分之一。
中位雇佣者是拥有学士或硕士学位的资深工程师,而不是研究科学家。那种“实验室里全是博士”的形象在工程层面大多是错误的。
研究领域 skewed(偏向)完全符合你对一个建造者组织的预期:计算机科学 819 人,然后是数学 78 人,物理学 70 人,计算机工程 69 人。哲学进入了前 20(13 人)(因为 AI 安全?)。
## 斯坦福在招聘中占据主导地位

学校(总计):Stanford 144 人,Berkeley 118 人,MIT 80 人,CMU 73 人,Harvard 42 人,Cambridge 39 人,UW 36 人,Waterloo 和 Cornell 各 35 人,Oxford 33 人,Princeton 32 人。仅前四名学校就占了组织的四分之一。
## 他们中 80% 的人共享同一个职位头衔。
“Member of Technical Staff”(技术专员)。
前 Instagram CTO、前 Adept 创始人以及斯坦福教员统统都是“MoTS”。这个头衔是故意扁平化的。资历和职能在设计中是不可见的。
## “初级”人员进入的唯一途径。

172 名工程师的从业经验少于六年。50 人少于三年。他们不是普通的应届毕业生。他们分为两种原型,中间几乎没有普通的中级人员。
看看他们与组织整体有何不同。博士更多(19% 对比 13.7%)。产品/SWE 头衔的比例是三倍(15% 对比 5%)。拥有 FAANG 简历的可能性要小得多(32% 对比 50%)。
他们取而代之的是可以替代年限的 pedigree(显赫背景):
- 实习生输送管道。50% 的人列出了在以下公司的实习经历:Meta 16 人,Google 10 人,DeepMind 6 人,Microsoft 5 人,Amazon 5 人,加上 Jane Street、Two Sigma、HRT、Optiver、Nvidia。
- 从量化交易到实验室。9% 的人来自精英交易公司。年轻的数据/CS 竞赛类型人才通过高频交易(HFT)进入。
- 对齐研究员奖学金。6% 的人接触过 MATS、SERI、Redwood 或 ARC。这是一个仅限初级人员的入口,在资深人员群体中几乎不存在。
清晰的典型形象:MIT,IOI 银牌,Codeforces 2900+ 分,在四年后直接进入强化学习和安全领域。他们是通过竞赛排名和论文来筛选的,而不是通过工作年限。
他们也比资深人员更具国际色彩。初级人员所在学校:Berkeley 15 人,Stanford 14 人,Cambridge 10 人,MIT 7 人,Tsinghua 7 人,Oxford 6 人,加上 Imperial、NUS、上海交通大学、ETH Zürich。
## 那么你能用这些信息做什么呢?
如果你想作为一名工程师加入 Anthropic,停止为研究实验室写简历,转而为基础设施公司写简历。展示你真正构建和扩展过的系统。这才是能被录用的简历。职业生涯早期是唯一的例外,而在那里的门槛是顶级实习经历、竞赛排名或论文。
如果你在招聘时与它们竞争,你的目标不是博士或实验室的徽标。而是来自超大规模云服务商或拥有基础设施声誉公司的资深建造者,拥有十二年的深厚经验。Stripe、Databricks、Snowflake、Palantir。Anthropic 已经在疯狂地从这个池子里“捕鱼”了。
---- :)
我正在为 A16z、Sequoia、YC、Founders Fund、Khosla 等支持的初创公司招聘工程师。从 Pre-seed 到 C 轮,地点在 NYC 和 SF,中级到资深的产品和 AI 工程师,薪资范围从 $150k - $400k。
我们会在一天内告知您是否有适合您的技能和兴趣的职位。
向我们的 iMessage 代理发短信 → 646-236-3745,看看谁想与您交谈(以及他们愿意付给您多少,而不仅仅是薪资范围)→ talent.fonzi.ai/home
## 相关链接
- [seb](https://x.com/hiiinternet)
- [@hiiinternet](https://x.com/hiiinternet)
- [117K](https://x.com/hiiinternet/status/2065117819948437765/analytics)
- [talent.fonzi.ai/home](https://talent.fonzi.ai/home)
- [升级到 Premium](https://x.com/i/premium_sign_up)
- [1:03 AM · Jun 12, 2026](https://x.com/hiiinternet/status/2065117819948437765)
- [117.8K Views](https://x.com/hiiinternet/status/2065117819948437765/analytics)
- [查看引用](https://x.com/hiiinternet/status/2065117819948437765/quotes)
---
*导出时间: 2026/6/12 12:11:03*