Own one full matching cycle end to end: take in live roles from hiring organisations, score candidates against them, run the introductions, and write up what the round revealed about how these jobs actually get filled.
About the project
AI safety organisations hire chiefs of staff and operations generalists constantly, and mostly fill those roles through warm introductions — so the pool for a given job is roughly whoever the founder already knows. That is a bad way to fill one of the highest-leverage seats in a small organisation. High Impact CoS exists to widen it: over 400 scored candidates, and working relationships with hiring organisations on the other side.
The binding constraint is not the pipeline; it is that someone has to actually run a round, and rounds currently happen when the mentor has time. September to December is one full round, and the mentee runs it — owning the cycle, making the calls about who goes forward, and writing the public account of what happened, rather than assisting on someone else's project. The mentor supplies the pipeline, the relationships and the introductions; High Impact CoS is a project the mentor runs, and the mentee is named author of the retrospective and keeps the relationships they build.
Theory of change
A good operations hire changes what a ten-person organisation can attempt; a bad one costs it a year. The current default selects on proximity to the founder rather than on fit, so every placement from a wider, better-scored pool is a real counterfactual improvement — and the retrospective is what makes the next round cheaper for anyone to run.
Your role
Talk to 8–12 hiring organisations about the role they are actually trying to fill, which is reliably not the role in the posting. Apply the existing rubric to the pipeline, and improve it where those conversations show it is measuring the wrong thing. Build a shortlist per role, make the case to both sides, then run the introductions and follow through rather than handing over a list. Finally, write the public retrospective on how chief-of-staff roles in AI safety get defined, scoped and filled, posted to the EA Forum and LessWrong.
Targets for the round:
- 8–12 organisation conversations
- 3+ live introductions
- at least one placement in progress by mid-December
- one published write-up
Placements depend on hiring timelines nobody controls, so the round is judged on introductions made and on the quality of the write-up, not on offers signed.
Prerequisites
Comfort talking to founders and senior operators, and asking the second and third question rather than accepting the first answer. Judgment about people, and willingness to make a recommendation you can be wrong about. No recruiting background required.
Application question(s)
Question 1: Find a current chief of staff or operations generalist posting at an AI safety organisation. Describe the person you would actually put in that role and why — including what you would need to know that the posting does not tell you. (~200 words, 300 words max)
About the mentor
Tzu Kit Chan is an AI safety fieldbuilder and operations generalist based in Berkeley. He runs High Impact CoS, a chief-of-staff talent pipeline for AI safety organisations, has seeded university AI safety groups across North America, Europe and Asia, and co-leads the 2026 update to Open Problems in Technical AI Governance. He was previously Chief of Staff at Atlas Computing.