A Field Guide to AI Fellowships
If you’re early in AI and you keep hearing “you should apply to a fellowship” without anyone telling you which one, for what, or how — this is for you. It’s the map I wish someone had handed me.
A bit of where I’m coming from: I recently went through the Anthropic Fellows program (AI safety), and before that I was exactly where you might be now — interested, a little overwhelmed, not sure what the tracks even were. So I wrote down the whole landscape the way I’d explain it to a friend over coffee. No gatekeeping, no jargon for its own sake.
How to use this
Every
[[blue link]]opens another note — this is a web, not a checklist. Start with the tracks, find the two or three that pull at you, then follow the links into the specifics. Don’t try to read it all at once.
Start with the map
- Tracks Overview — the different kinds of AI fellowship work, and which one might be you
- Skills Map — what each track actually requires (and an honest self-audit)
Then get specific
- Programs Directory — the real programs, with real details (stipends, timing, who they’re for)
- Key Papers — the specific papers worth your time, grouped by track
- Reading and Courses — the courses, curricula, and resources I’d actually recommend
Reference
- Glossary — every bit of jargon, demystified
The tracks, directly
Research Scientist · Research Engineer · Interpretability · Alignment and AI Safety · Policy and Governance · Applied and Product ML · Field-building and Comms
The one idea underneath all of this
Reviewers grade evidence, not potential. You don’t get into these programs by being interested — you get in by making one small, finished, legible thing and pointing it at the right track. Everything in this vault is in service of that. (The portfolio mindset)
About this guide
Free to use and share. If it helped, pass it on. Corrections and additions welcome. Program details change every cycle — always verify on the official page before you act.