careerladder.fyi › Career paths › Meta
How people got jobs at Meta
What 178 people did before they joined Meta: where they worked, what they studied, which role they came in through, and how long it took.
178
people in our data got there
71%
had a graduate degree first (of those listing one)
8
typical years since first job
- The most common step right before: from other companies (59%).
- The most common way in was Research (27% joined in that kind of role).
- Of those who list their education, 71% had a graduate degree before arriving and 24% a PhD.
- The typical person arrived about 8 years after their first job.
- The most common earlier employer: Microsoft (14% had worked there).
How Meta compares
- 24% had a PhD, above the 16% across other Big Tech companies, the 3rd-highest of the 12 in our data.
- 71% had a graduate degree, above the 60% across other Big Tech companies, the 3rd-highest of the 12 in our data.
- 22% came straight from Big Tech, above the 13% across other Big Tech companies, the 2nd-highest of the 12 in our data.
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 (this page) | 103 | 71% |
| 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 (this page) | 8 yrs | |
| Palantir Technologies | 8 yrs | |
| 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 | |
|---|---|---|
| Research | 48 | 27% |
| Software engineering | 46 | 26% |
| All other roles | 42 | 24% |
| Data science & ML | 17 | 10% |
| Business & operations | 13 | 7% |
| Product management | 7 | 4% |
| Sales & business development | 5 | 3% |
Show as a table
| People | Share | |
|---|---|---|
| Software Engineer | 19 | 11% |
| Research Scientist | 13 | 7% |
| Research | 10 | 6% |
| Software Engineering | 7 | 4% |
| Research Engineer | 5 | 3% |
What they did right before
Show as a table
| People | Share | |
|---|---|---|
| From other companies | 105 | 59% |
| From Big Tech | 40 | 22% |
| Publishing research | 12 | 7% |
| From banks & Fortune 500 | 10 | 6% |
| This was their first listed step | 5 | 3% |
| Other | 6 | 3% |
Where they worked before
A person can count under more than one employer.
Show as a table
| People | Share | |
|---|---|---|
| Microsoft | 25 | 14% |
| 19 | 11% | |
| Amazon | 10 | 6% |
| Amazon Web Services (AWS) | 9 | 5% |
| Apple | 8 | 4% |
| 5 | 3% | |
| Carnegie Mellon University | 5 | 3% |
| Oracle | 5 | 3% |
Education
Among the 103 of 178 people who list their education.
Show as a table
| People | Share | |
|---|---|---|
| Bachelor's | 30 | 29% |
| Master's | 42 | 41% |
| MBA | 6 | 6% |
| PhD | 25 | 24% |
A person can count under more than one school.
Show as a table
| People | Share | |
|---|---|---|
| Columbia University | 5 | 3% |
How much experience they had
Among the 170 of 178 people with an earlier job listed.
Show as a table
| People | Share | |
|---|---|---|
| Under 1 year | 6 | 4% |
| 1-3 years | 24 | 14% |
| 3-5 years | 13 | 8% |
| 5-8 years | 38 | 22% |
| 8-12 years | 34 | 20% |
| 12+ years | 55 | 32% |
Where would your path lead?
Paste your LinkedIn or resume and see the ladders people like you climbed to Meta, and how each step changes your chances.
Plan my path to Meta →
Paste your LinkedIn or resume and see the ladders people like you climbed to Meta, and how each step changes your chances.
Plan my path to Meta →
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.