How much is a track record of conducting relevant research valued for getting a policy job at OpenAI? How does this compare to having (additional) prestigious qualifications and prestigious job experience?
I also wanted to pass along this explanation from our team manager, Jack Clark:
OpenAI’s policy team looks for candidates that display an ‘idiosyncratic specialism’ along with verifiable interest and intuitions regarding technical aspects of AI technology; members of OpenAI’s team currently have specialisms ranging from long-term TAI-oriented ethics, to geopolitics of compute, to issues of representation in generative models, to ‘red teaming’ technical systems from a security perspective, and so on. OpenAI hires people with a mixture of qualifications, and is equally happy hiring someone with no degrees and verifiable industry experience, as well as someone with a PHD. At OpenAI, technical familiarity is a prerequisite for successful policy work, as our policy team does a lot of work that involves embedding alongside technical teams on projects (see: our work throughout 2019 on GPT2).
I’m not involved in hiring at OpenAI, so I’m going to answer more in the spirit of “advice I would give for people interested in pursuing a career in EA AI policy generally.”
In short, I think actually trying your hand at the research is probably more valuable on the margin, especially if it yields high-quality research. (And if you discover it’s not a good fit, that’s valuable information as well.) This is basically what happened to me during my FHI internship: I found out that I was a good fit for this work, so I continued on in this path. There are a lot of very credentialed EAs, but (for better or worse), many EA AI policy careers take a combination of hard-to-describe and hard-to-measure skills that are best measured by actually trying to do it. Furthermore, there is unfortunately a managerial bottleneck in this space: there are far more people interested in entering it than people that can supervise potential entrants. I think it can be a frustrating space to enter; I got very lucky in many ways during my path here.
So, if you can’t actually try the research in a supervised setting, cultivating general skills or doing adjacent research (e.g., general AI policy) is a good step too. There are always skills I wish I had (and which I am fortunate to get to cultivate at OpenAI during Learning Day). Some of the stuff I studied during Learning Day which might guide your own skill cultivation include:
How much is a track record of conducting relevant research valued for getting a policy job at OpenAI? How does this compare to having (additional) prestigious qualifications and prestigious job experience?
I also wanted to pass along this explanation from our team manager, Jack Clark:
I’m not involved in hiring at OpenAI, so I’m going to answer more in the spirit of “advice I would give for people interested in pursuing a career in EA AI policy generally.”
In short, I think actually trying your hand at the research is probably more valuable on the margin, especially if it yields high-quality research. (And if you discover it’s not a good fit, that’s valuable information as well.) This is basically what happened to me during my FHI internship: I found out that I was a good fit for this work, so I continued on in this path. There are a lot of very credentialed EAs, but (for better or worse), many EA AI policy careers take a combination of hard-to-describe and hard-to-measure skills that are best measured by actually trying to do it. Furthermore, there is unfortunately a managerial bottleneck in this space: there are far more people interested in entering it than people that can supervise potential entrants. I think it can be a frustrating space to enter; I got very lucky in many ways during my path here.
So, if you can’t actually try the research in a supervised setting, cultivating general skills or doing adjacent research (e.g., general AI policy) is a good step too. There are always skills I wish I had (and which I am fortunate to get to cultivate at OpenAI during Learning Day). Some of the stuff I studied during Learning Day which might guide your own skill cultivation include:
Machine Learning
Economic Development
Financial Accounting
Basic Economics