Andy Jones
Current spec is that you should spend at least 25% of the time in SF per month. We’ve a slight preference for folks who can be here full-time, but that’s easily overwhelmed by a promising candidate.
It certainly loses us some talent. So far the feeling is that it’s worth it for the cultural benefits, but that might change in future. We’ve definitely noticed that ‘similar timezone’ is the majority of the friction in folks working remotely, so that might be the thing to specify rather than explicitly being on-site.
nah i just accidentally a word. fixed!
To provide a contrasting view, I surveyed the background of Anthropic’s technical staff a while ago.
12 out of 24 had PhDs.
Of those 12, 9 were in physics, two were in philosophy, and one was in biology.
Of the 12 without PhDs, the plurality were CS graduates, with the rest being a mix of physics, maths, engineering and biology.
Also one GED.
In particular, we had no ML PhDs as of when the survey was done (though we’ve hired two since!). I think Anthropic is an unusual organisation and our demographics won’t generalise well to the broader community, but I do think it’s representative of the ongoing shift to more empirical work.
The paperwork required to be entered into the lottery is almost trivial—see the step-by-step instructions here. Most orgs will want an immigration lawyer to do it though because while it’s an easy first step, it’s an easy first step in a long and difficult process. If an org isn’t used to handling H-1B cases, I expect the huge hangup will be finding and retaining an immigration lawyer in the first place.
Several folk joined OpenAI this way when it was a charity, and more recently I believe Redwood has been using it too.
Thanks! I’ve edited in your corrections.
Hand-in-hand with that, Anthropic is hiring, especially for great engineers. And we sponsor visas!
If you’re after a job in the US: the H-1B Lottery is in six weeks
‘crazy’ as in ‘willing to pay a ridiculous CoL premium that everyone outside of these very specific regions will look askance at you for’.
By ‘the community’ I don’t mean just EA but the larger pool of driven young university grads who EA draws from. It’s no accident that EA’s Schelling points for talent largely match those of the wider world.
As a recent worked example in shifting these Schelling points, take MIRI’s proposal to move away from the Bay Area. It didn’t work out, and they had a better chance of making this work than most.
That said, I still do I think the best opportunities for establishing new Schelling points are around insular work-areas like MIRI. I think the animal rights nexus forming in Berlin is another good example.
I think it’s a combination of
lack of awareness—which this post was aimed at solving
misalignment in skillset - not particularly ML, but numerical and distributed systems experience
our specifically looking for very, very good software engineers
My personal experience is that I wish I’d moved to London sooner, and I wish I’d moved to SF sooner. The CoL is dwarfed by access to capital and access to the Schelling points of the global community.
This has a strange side effect: you have to be crazy to want to live in London or SF. Consequently, only crazy people live in London or SF! And those are in fact the people I’m most keen to spend my time with.
I worked as a quant trader and a ML researcher though, so my perspective is particularly skewed.
I’m hoping to have some comprehensive advice out on this Soon(TM), but my sense is that if you’re familiar with multivariable calculus/linear algebra/probability then it’s a few months’ full-time-work-equivalent to skill up in ML to the point where it’s no longer the constraining factor in being hired as an ML engineer.
If you’re not familiar with multivar calc/linear algebra/probability then it’s a bit of a longer path and a harder one to predict, since I think ease-with-maths varies more widely than ease-with-ML.
I appreciate the feedback, but the spec is intentionally over-broad rather than over-narrow. I and several other engineers in AI safety have made serious efforts to try and pin down exactly what ‘great software engineering’ is, and—for want of a better phrase—have found ourselves missing the forest for the trees. What we’re after is a certain level of tacit, hard-to-specify skills and knowledge that we felt was best characterised by the litmus test given above.