careerladder.fyi › Career paths › Google
How people got jobs at Google
What 207 people did before they joined Google: where they worked, what they studied, which role they came in through, and how long it took.
207
people in our data got there
69%
had a graduate degree first (of those listing one)
7
typical years since first job
- The most common step right before: from other companies (57%).
- The most common way in was Software engineering (33% joined in that kind of role).
- Of those who list their education, 69% had a graduate degree before arriving and 22% a PhD.
- The typical person arrived about 7 years after their first job.
- The most common earlier employer: Microsoft (11% had worked there).
How Google compares
- On these measures, Google is close to typical for Big Tech companies.
Among people who list their education; places with at least 10 such people.
Show as a table
| People listing education | Share | |
|---|---|---|
| Apple | 50 | 78% |
| 16 | 75% | |
| Meta | 103 | 71% |
| Google (this page) | 105 | 69% |
| NVIDIA | 36 | 67% |
| Amazon | 54 | 63% |
| Adobe | 43 | 58% |
| Microsoft | 97 | 58% |
| Amazon Web Services (AWS) | 44 | 50% |
| Palantir Technologies | 36 | 50% |
| Salesforce | 28 | 43% |
| Oracle | 30 | 37% |
Show as a table
| Median years | ||
|---|---|---|
| Adobe | 12 yrs | |
| Amazon Web Services (AWS) | 11 yrs | |
| Salesforce | 11 yrs | |
| NVIDIA | 10 yrs | |
| Apple | 10 yrs | |
| Meta | 8 yrs | |
| Palantir Technologies | 8 yrs | |
| Google (this page) | 7 yrs | |
| Microsoft | 7 yrs | |
| Oracle | 7 yrs | |
| Amazon | 6 yrs | |
| 6 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 | |
|---|---|---|
| Software engineering | 68 | 33% |
| Research | 34 | 16% |
| All other roles | 50 | 24% |
| Product management | 12 | 6% |
| Business & operations | 11 | 5% |
| Data science & ML | 11 | 5% |
| Sales & business development | 10 | 5% |
| Project program mgmt | 6 | 3% |
| IT & infrastructure | 5 | 2% |
Show as a table
| People | Share | |
|---|---|---|
| Software Engineer | 31 | 15% |
| Research Scientist | 12 | 6% |
| Software Engineering | 11 | 5% |
| Research | 8 | 4% |
What they did right before
Show as a table
| People | Share | |
|---|---|---|
| From other companies | 118 | 57% |
| From Big Tech | 38 | 18% |
| From banks & Fortune 500 | 17 | 8% |
| This was their first listed step | 12 | 6% |
| Publishing research | 10 | 5% |
| From frontier AI labs | 5 | 2% |
| Other | 7 | 3% |
Where they worked before
A person can count under more than one employer.
Show as a table
| People | Share | |
|---|---|---|
| Microsoft | 22 | 11% |
| Meta | 12 | 6% |
| Amazon | 11 | 5% |
| IBM | 11 | 5% |
| Apple | 7 | 3% |
| Amazon Web Services (AWS) | 7 | 3% |
| Stanford University | 6 | 3% |
| University of Illinois at Urbana-Champaign | 5 | 2% |
Education
Among the 105 of 207 people who list their education.
Show as a table
| People | Share | |
|---|---|---|
| Bachelor's | 33 | 31% |
| Master's | 44 | 42% |
| MBA | 5 | 5% |
| PhD | 23 | 22% |
A person can count under more than one school.
Show as a table
| People | Share | |
|---|---|---|
| NYU | 7 | 3% |
| Georgia Tech | 6 | 3% |
| USC | 5 | 2% |
| UIUC | 5 | 2% |
| Stanford University | 5 | 2% |
| Columbia University | 5 | 2% |
How much experience they had
Among the 192 of 207 people with an earlier job listed.
Show as a table
| People | Share | |
|---|---|---|
| Under 1 year | 3 | 2% |
| 1-3 years | 32 | 17% |
| 3-5 years | 22 | 11% |
| 5-8 years | 43 | 22% |
| 8-12 years | 41 | 21% |
| 12+ years | 51 | 27% |
Where would your path lead?
Paste your LinkedIn or resume and see the ladders people like you climbed to Google, and how each step changes your chances.
Plan my path to Google →
Paste your LinkedIn or resume and see the ladders people like you climbed to Google, and how each step changes your chances.
Plan my path to Google →
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.