careerladder.fyi › Career paths › Anthropic
How people got jobs at Anthropic
What 56 people did before they joined Anthropic: where they worked, what they studied, which role they came in through, and how long it took.
56
people in our data got there
54%
had a graduate degree first (of those listing one)
15
typical years since first job
- The most common step right before: from other companies (29%).
- The most common way in was Business & operations (39% joined in that kind of role).
- Of those who list their education, 54% had a graduate degree before arriving and 14% a PhD.
- The typical person arrived about 15 years after their first job.
- The most common earlier employer: Google (27% had worked there).
How Anthropic compares
- 54% had a graduate degree, below the 67% across other frontier AI labs.
- 29% came straight from Big Tech, above the 17% across other frontier AI labs.
- People arrived about 2 years later in their careers than at the typical one of the other frontier AI labs (15 vs 13 years since their first job).
Among people who list their education; places with at least 10 such people.
Show as a table
| People listing education | Share | |
|---|---|---|
| Google DeepMind | 58 | 41% |
| World Labs | 39 | 33% |
| Physical Intelligence | 31 | 32% |
| Sakana AI | 41 | 29% |
| Cohere | 40 | 28% |
| Hugging Face | 34 | 24% |
| Stability AI | 40 | 22% |
| SpaceXAI | 17 | 18% |
| Thinking Machines Lab | 29 | 17% |
| OpenAI | 41 | 17% |
| Anthropic (this page) | 28 | 14% |
| Black Forest Labs | 32 | 12% |
| EleutherAI | 26 | 12% |
| Reflection | 20 | 10% |
| Mistral AI | 36 | 8% |
| Skild AI | 39 | 8% |
| Poolside | 31 | 6% |
| Cognition | 27 | 4% |
| Perplexity | 24 | <1% |
Show as a table
| Median years | ||
|---|---|---|
| SpaceXAI | 18 yrs | |
| Anthropic (this page) | 15 yrs | |
| Mistral AI | 15 yrs | |
| OpenAI | 14 yrs | |
| Perplexity | 14 yrs | |
| Reflection | 14 yrs | |
| Thinking Machines Lab | 14 yrs | |
| Stability AI | 14 yrs | |
| Poolside | 13 yrs | |
| Cognition | 13 yrs | |
| Cohere | 13 yrs | |
| Black Forest Labs | 10 yrs | |
| Physical Intelligence | 10 yrs | |
| Google DeepMind | 10 yrs | |
| Sakana AI | 9 yrs | |
| Skild AI | 9 yrs | |
| World Labs | 8 yrs | |
| Hugging Face | 8 yrs | |
| EleutherAI | 5 yrs |
Which role they joined in
Kinds of role with fewer than 5 people are combined into "All other roles".
Show as a table
| People | Share | |
|---|---|---|
| Business & operations | 22 | 39% |
| Member of technical staff | 7 | 12% |
| Data science & ML | 6 | 11% |
| Sales & business development | 5 | 9% |
| All other roles | 16 | 29% |
Show as a table
| People | Share | |
|---|---|---|
| GTM | 6 | 11% |
| Member of Technical Staff | 6 | 11% |
What they did right before
Show as a table
| People | Share | |
|---|---|---|
| From other companies | 16 | 29% |
| From Big Tech | 16 | 29% |
| From frontier AI labs | 10 | 18% |
| From fintech | 6 | 11% |
| Other | 8 | 14% |
Where they worked before
A person can count under more than one employer.
Show as a table
| People | Share | |
|---|---|---|
| 15 | 27% | |
| Salesforce | 10 | 18% |
| Stripe | 9 | 16% |
| OpenAI | 7 | 12% |
| Microsoft | 5 | 9% |
Education
Among the 28 of 56 people who list their education.
Show as a table
| People | Share | |
|---|---|---|
| Bachelor's | 13 | 46% |
| Master's | 7 | 25% |
| MBA | 4 | 14% |
| PhD | 4 | 14% |
How much experience they had
Among the 56 of 56 people with an earlier job listed.
Show as a table
| People | Share | |
|---|---|---|
| 3-5 years | 2 | 4% |
| 5-8 years | 5 | 9% |
| 8-12 years | 6 | 11% |
| 12+ years | 43 | 77% |
Where would your path lead?
Paste your LinkedIn or resume and see the ladders people like you climbed to Anthropic, and how each step changes your chances.
Plan my path to Anthropic →
Paste your LinkedIn or resume and see the ladders people like you climbed to Anthropic, and how each step changes your chances.
Plan my path to Anthropic →
Related
How these numbers are made. From 3k+ public career histories collected for careerladder.fyi (dated jobs, degrees and publications). The collection was built around particular employers, so people who reached well-known tech, AI and finance employers are over-represented: read shares as "among people in this dataset", not as hiring odds. Names are shown only when at least 5 people are behind them; smaller groups are combined into "Other". No individual profile is shown. Updated November 2026.