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Per Andy Jones over at LessWrong:
If you think you could write a substantial pull request for a major machine learning library, then major AI safety labs want to interview you today.
I work for Anthropic, an industrial AI research lab focussed on safety. We are bottlenecked on aligned engineering talent. Specifically engineering talent. While we'd always like more ops folk and more researchers, our safety work is limited by a shortage of great engineers.
I've spoken to several other AI safety research organisations who feel the same.
I'm not sure what you mean by "AI safety labs", but Redwood Research, Anthropic, and the OpenAI safety team have all hired self-taught ML engineers. DeepMind has a reputation for being more focused on credentials. Other AI labs don't do as much research that's clearly focused on AI takeover risk.
I'm currently at DeepMind and I'm not really sure where this reputation has come from. As far as I can tell DeepMind would be perfectly happy to hire self-taught ML engineers for the Research Engineer role (but probably not the Research Scientist role; my impression is that this is similar at other orgs). The interview process is focused on evaluating skills, not credentials.
DeepMind does get enough applicants that not everyone makes it to the interview stage, so it's possible that self-taught ML engineers are getting rejected before getting a chance to show they know ML. But presumably this is also a problem that Redwood / Anthropic / OpenAI have? Presumably there is some way that self-taught ML engineers are signaling that they are worth interviewing. (As a simple example, if I personally thought someone was worth interviewing, my recommendation would function as a signal for "worth interviewing", and in that situation DeepMind would interview them, and at that point I predict their success would depend primarily on their skills and not their credentials.)
If there's some signal of "worth interviewing" that DeepMind is failing to pick up on, I'd love to know that; it's the sort of problem I'd expect DeepMind-the-company to want to fix.
DeepMind doesn’t hire people without PhDs as research scientists
Basically true (though technically the requirement is "PhD in a technical field or equivalent practical experience")
places more restrictions on what research engineers can do than other places
Doesn't seem true to me. Within safety I can name two research engineers who are currently leading research projects.
DeepMind might be more explicit that in practice the people who lead research projects will tend to have PhDs. I think this pattern is just because usually people with PhDs are better at leading research projects than people without PhDs. I expect to see the same pattern at OpenAI and Anthropic. If I assigned people to roles based solely on (my evaluation of) capabilities / merit, I'd expect to reproduce that pattern.
"DeepMind allows REs to lead research projects" is consistent with "DeepMind restricts REs more than other places". E.g. OpenAI doesn't even officially distinguish RE from RS positions, whereas DeepMind has different ladders with different expectations for each. And I think the default expectations for REs and RSs are pretty different (although I agree that it's possible for REs to end up doing most of the same things as RSs).
I continue to think that this is primarily a reflection of RSs having more experience than REs, and that a process with a single role and no RS / RE distinction would produce similar outcomes given the same people.
As I understand it, DeepMind doesn’t hire people without PhDs as research scientists, and places more restrictions on what research engineers can do than other places.