This is a hard question to answer, because there are so many different jobs someone could take in the GCR space that might be really impactful. And while we have a good sense of what someone can achieve by working at OP, we can't easily compare that to all the other options someone might have. A comparison like "OP vs. grad school" or "OP vs. pursuing a government career" comes with dozens of different considerations that would play out differently for any specific person.

Ultimately, we hope people will consider jobs we've posted (if they seem like a good fit), and also consider anything else that looks promising to them.

Generally, we try to compensate people in such a way that compensation is neither the main reason to be at Open Phil nor the main reason to consider leaving. We rely on market data to set compensation for each role, aiming to compete with a candidate’s “reasonable alternatives” (e.g., other foundations, universities, or high-end nonprofits; not roles like finance or tech where compensation is the main driving factor in recruiting). Specifically, we default to using a salary survey of other large foundations (Croner), and currently target the 75th percentile, as well as offering modest upwards adjustments on top of the base numbers for staff in SF and DC (where we think there are positive externalities for the org from staff being able to cowork in person, but higher cost of living). I can’t speak to what they’re currently doing, but historically, GiveWell has used the same salary survey; I’d guess that the Senior Research role is benchmarked to Program Officer, which is a more senior role than we’re currently posting for in this GCR round, which explains the higher compensation. I don’t know what BMGF benchmarks you are looking at, but I’d guess you’re looking at more senior positions that typically require more experience and control higher budgets at the higher end.

That said, your point about technical AI Safety researchers at various nonprofit orgs making more than our benchmarks is something that we’ve been reflecting on internally and think does represent a relevant “reasonable alternative” for the kinds of folks that we’re aiming to hire, and so we’re planning to create a new comp ladder for technical AI Safety roles, and in the meantime have moderately increased the posted comp for the open TAIS associate and senior associate roles.

Thanks! I've added a direct link to the roles now, to reduce potential confusion.

I don’t have specific data on this, but only a minority of our hires apply through referrals or active networking. Our process is set up to avoid favoring people who come via prior connections (e.g. by putting a lot of weight on anonymized work tests), and many of our best hires have joined without any prior connections to OP. However, we do still get a lot of value from people referring candidates to our roles, and would encourage more of this to keep expanding our networks.

(FYI to others - I've just seen Ajeya's very helpful writeup, which has already partially answer this question!)

The technical folks leading our AI alignment grantmaking (Daniel Dewey and Catherine Olsson) left to do more "direct" work elsewhere a while back, and Ajeya only switched from a research focus (e.g. the Bio Anchors report) to an alignment grantmaking focus late last year. She did some private recruiting early this year, which resulted in Max Nadeau joining her team very recently, but she'd like to hire more. So the answer to "Why now?" on alignment grantmaking is "Ajeya started hiring soon after she switched into a grantmaking role. Before that, our initial alignment grantmakers left, and it's been hard to find technical folks who want to focus on grantmaking rather than on more thoroughly technical work."

Re: the governance team. I've lead AI governance grantmaking at Open Phil since ~2019, but for a few years we felt very unclear about what our strategy should be, and our strategic priorities shifted rapidly, and it felt risky to hire new people into a role that might go away through no fault of their own as our strategy shifted. In retrospect, this was a mistake and I wish we'd started to grow the team at least as early as 2021. By 2022 I was finally forced into a situation of "Well, even if it's risky to take people on, there is just an insane amount of stuff to do and I don't have time for ~any of it, so I need to hire." Then I did a couple non-public hiring rounds which resulted in recent new hires Alex Lawsen, Trevor Levin, and Julian Hazell. But we still need to hire more; all of us are already overbooked and turning down opportunities for lack of bandwidth constantly.

Holden has been working on independent projects, e.g. related to RSPs; the AI teams at Open Phil no longer report to him and he doesn't approve grants. We all still collaborate to some degree, but new hires shouldn't e.g. expect to work closely with Holden.

Yeah, I feel a lot of this stress as well, though FWIW for me personally research was more stressful. I don't think there's any crisp institutional advice or formula for dealing with this kind of thing unfortunately. One disposition that I think makes it hard to be a grantmaker at OP (in addition to your list, which I think is largely overlapping) is being overly attached to perfection and satisfyingly clean, beautifully-justifiable answers and decisions.

We fund a lot of groups and individuals and they have a lot of different (and sometimes contradicting) policy opinions, so the short answer is "yes." In general, I really did mean the "tentative" in my 12 tentative ideas for US AI policy, and the other caveats near the top are also genuine.

That said, we hold some policy intuitions more confidently than others, and if someone disagreed pretty thoroughly with our overall approach and they also weren't very persuasive that their alternate approach would be better for x-risk reduction, then they might not be a good fit for the team.

For me personally, research and then grantmaking at Open Phil has been excellent for my career development, and it's pretty implausible that grad school in ML or CS, or an ML engineering role at an AI company, or any other path I can easily think of, would have been comparably useful. 

If I had pursued an academic path, then assuming I was successful on that path, I would be in my first or maybe second year as an assistant professor right about now (or maybe I'd just be starting to apply for such a role). Instead, at Open Phil, I wrote less-academic reports and posts about less established topics in a more home-grown style, gave talks in a variety of venues, talked to podcasters and journalists, and built lots of relationships in industry, academia, and the policy world in the course of funding and advising people. I am likely more noteworthy among AI companies, policymakers, and even academic researchers than I would have been if I had spent that time doing technical research in a grad school and then went for a faculty role — and I additionally get to direct funding, an option which wouldn't have been easily available to me on that alternative path.

The obvious con of OP relative to a path like that is that you have to "roll your own" career path to a much greater degree. If you go to grad school, you will definitely write papers, and then be evaluated based on how many good papers you've written; there isn't something analogous you will definitely be made to do and evaluated on at OP (at least not something clearly publicly visible). But I think there are a lot of pros:

• The flipside of the social awkwardness and stress that Linch highlighted in one of his questions is that a grantmaking role teaches you how to navigate delicate power dynamics, say no, give tough feedback, and make non-obvious decisions that have tangible consequences on reasonably short timeframes. I think I've developed more social maturity and operational effectiveness than I would have in a research role; this is a pretty important and transferrable skillset.
• There is more space than there would be in a grad school or AI lab setting to think about weird questions that sit at the intersection of different fields and have no obvious academic home, such as the trajectory of AI development and timelines to very powerful AI. While independent research or other small-scale nonprofit research groups could offer a similar degree of space to think about "weird stuff," OP is unusual in combining that kind of latitude with the ability to direct funding (and thus the ability to help make big material projects happen in the world).
   

Professors typically have their own salaries covered, but need to secure funding for each new student they take on, so providing funding to an academic lab allows them to take on more students and grow (it's not always the case that everyone is taking on as many students as they can manage). Additionally, it's often hard for professors to get funding for non-student expenses (compute, engineering help, data labeling contractors, etc) through NSF grants and similar, which are often restricted to students.

On technical AI safety, fundamentally having more grantmaking and research capacity (junior or senior) will help us make more grants to great projects that we wouldn't have been able to otherwise; I wrote about that team's hiring needs in this separate post. In terms of AI safety more broadly (outside of just my team), I'd say there is a more severe constraint on people who can mentor junior researchers, but the field could use more strong researchers at all levels of seniority.

Hi Vaipan, I’ll take your question about the ratio of hires between AI safety and biosecurity. In short, no, it wouldn’t be correct to conclude that biosecurity is more talent constrained than AI safety. The number of roles is rather a reflection of our teams’ respective needs at the given moment.

And on the “more diverse consideration about GCR” question, note that my team is advertising for a contractor who will look into risks that lie outside biosecurity and AI safety, including nuclear weapons risks. Note though that I expect AI safety and biosecurity to remain more highly prioritized going forward.

Hi Vaipan,

Thanks for your questions. I’ll address the last one, on behalf of the cause prio team.

One of the exciting things about this team is that, because it launched so recently, there’s a lot of room to try new things as we explore different ways to be useful. To name a few examples:

• We’re working on a constellation of projects that will help us compare our grantmaking focused on risks from advanced AI systems to our grantmaking focused on improving biosecurity and pandemic preparedness.
• We’re producing a slew of new BOTECs across different focus areas. If it goes well, this exercise will help us be more quantitative when evaluating and comparing future grantmaking opportunities.
• As you can imagine, the result of a given BOTEC depends heavily on the worldview assumptions you plug in. There isn’t an Open Phil house view on key issues like AI timelines or p(doom). One thing the cause prio team might do is periodically survey senior GCR leaders on important questions so we better understand the distribution of answers.
• We’re also doing a bunch of work that is aimed at increasing strategic clarity. For instance, we’re thinking a lot about next-generation AI models: how to forecast their capabilities, what dangers those capabilities might imply, how to communicate those dangers to labs and policymakers, and ultimately how to design evals to assess risk levels.

There is no particular background knowledge that is required for a role on our team. For context, a majority of current team members were working on global health and wellbeing issues less than a year ago. For this hiring round, applicants that understand the GCR ecosystem and have at least superficial understanding of frontier AI models will in general do better than applicants that lack that understanding. But I encourage everyone who is interested to apply.

I think this is definitely a real dynamic, but a lot of EAs seem to exaggerate it a lot in their minds and inappropriately round the impact of external research down to 0. Here are a few scattered points on this topic:

• Third party researchers can influence the research that happens at labs through the normal diffusion process by which all research influences all other research. There's definitely some barrier to research insight diffusing from academia to companies (and e.g. it's unfortunately common for an academic project to have no impact on company practice because it just wasn't developed with the right practical constraints in mind), but it still happens all the time (and some types of research, e.g. benchmarks, are especially easy to port over). If third party research can influence lab practice to a substantial degree, then funding third party research just straightforwardly increases the total amount of useful research happening, since labs can't hire everyone who could do useful work.  
• It will increasingly be possible to do good (non-interpretability) research on large models through APIs provided by labs, and Open Phil could help facilitate that and increase the rate at which it happens. We can also help facilitate greater compute budgets and engineering support.
• The work of the lab-external safety research community can also impact policy and public opinion; the safety teams at scaling labs are not their only audience. For example, capability evaluations and model organisms work both have the potential to have at least as big an impact on policy as they do on technical safety work happening inside labs.
• We can fund nonprofits and companies which directly interface with AI companies in a consulting-like manner (e.g. red-teaming consultants); I expect an increasing fraction of our opportunities to look like this.
• Academics and other external safety researchers we fund now can end up joining scaling labs later (as e.g. Ethan Perez and Collin Burns did), to implement ideas that they developed on the outside; I think this is likely to happen more and more.
• Some research directions benefit less than others from access to cutting edge models. For example, it seems like there's a lot of interpretability work that can be done on very small models, whereas scalable oversight work seems harder to do without quite smart models.

Echoing Eli: I've run ~4 hiring rounds at Open Phil in the past, and in each case I think if the top few applicants disappeared, we probably just wouldn't have made a hire, or made significantly fewer hires.

I'm not from Open Philantropy, but it's likely people worry too much about this.

There is certainly no defined age cutoff, and we are usually extra excited when we can hire candidates who bring many years of career experience to the table in addition to other qualifications!

Indeed. There aren't hard boundaries between the various OP teams that work on AI, and people whose reporting line is on one team often do projects for or with a different team, or in another team's "jurisdiction." We just try to communicate about it a lot, and our team leads aren't very possessive about their territory — we just want to get the best stuff done!

I'll just add that in a lot of cases, I fund technical research that I think is likely to help with policy goals (for example, work in the space of model organisms of misalignment can feed into policy goals).

We gather data on these topics through post-round candidate surveys, so I can share what candidates actually say rather than speculate! 

On the frustration / annoyance side:

• The most common thing that comes up is that we don’t share personalized feedback with unsuccessful candidates except for those who make it to the final stage of the process. This is a tricky problem that we’ve discussed a lot, as it’s not feasible for us to do this given the large number of candidates who apply. We’re trying to provide more generalized feedback on our work tests for certain roles as one possible improvement.
• Another thing that comes up is that our processes can be fairly long (often 2-3 months from when a candidate applies). We try to speed this up where possible but have to trade that off against having a large number of data points from work trials and interviews, which we also see as valuable.

On the ‘OP does that really well’ side:

• Candidates often praise the transparency of our process and what we’re looking for at each stage.
• Candidates usually enjoy their interactions with OP staff - we do our best to make most conversations a two-way street, and are less intimidating than I think some people imagine!

It's hard to project forward of course, but currently there are ~50 applicants to the TAIS team and ~100 to the AI governance team (although I think a number of people are likely to apply close to the deadline).

I'm very interested in these paths. In fact, I currently think that well over half the value created by the projects we have funded or will fund in 2023 will go through "providing evidence for dangerous capabilities" and "demonstrating emergent misalignment;" I wouldn't be surprised if that continues being the case.

The way I approach the role, it involves thinking deeply about what technical research we want to see in the world and why, and trying to articulate that to potential grantees (in one-on-one conversations, posts like this one, RFPs, talks at conferences, etc) so that they can form a fine-grained understanding of how we're thinking about the core problems and where their research interests overlap with Open Phil's philanthropic goals in the space. To do this well, it's really valuable to have a good grip on the existing work in the relevant area(s).

1. Check out "what kinds of qualities are you looking for in a hire" here. My sense is we index less on previous experience than many other organizations (though it's still important). Experience juggling many tasks, prioritize, and syncing up with stakeholders jumps to mind. I have a hypothesis that consultant experience would be helpful for this role, but that's a bit conjectural.
2. This is a bit TBD — happy to chat more further down the pipeline with any interested candidates.
3. We look for this in work tests and in previous experience.

Thanks Mishaal!

1. I think previous experience taking on operationally challenging projects is definitely the most important thing here, though it may not necessarily be traditional job experience (running a student group or local group can also provide good experience here). Beyond that, demonstrating pragmatism and worldliness in interviews (for example, when discussing real or hypothetical operational or time management challenges) is useful.
2. I think an important quality in a role like this is steadiness — not getting easily overwhelmed by juggling a lot of competing tasks, having the ability to get the easy stuff done quickly and make smart calls about prioritizing between the harder more nebulous tasks. And across all our roles, being comfortable with upward feedback and disagreement is key.

1. The CB team continuously evaluates the track record of grants we've made when they're up for renewal, and this feeds into our sense of how good programs are overall. We also spend a lot of time keeping up with what's happening in CB and in x-risk generally, and this feeds into our picture of how well CB projects are working.
2. Check out "what kinds of qualities are you looking for in a hire" here.
3. Same answer as 2.

1. Similar to that of our other roles, plus experience running a university group as an obvious one — I also think that extroversion and proactive communication are somewhat more important for these roles than for others.
2. Going to punt on this one as I'm not quite sure what is meant by "systems."
3. This is too big to summarize here, unfortunately.

As highlighted in the job descriptions, the answer for what we’re looking for in both skills and experiences varies from role to role (i.e., operations ≠ grantmaking ≠ research). When we evaluate candidates, though, we usually are less asking “What degree do they have? How many years have they worked in this field?” and more asking “What have they accomplished so far (relative to their career stage)? Do they have the skills we’re looking for? Will they be a good fit with our team and help meet an ongoing need? Do we have reason to expect them to embody Open Philanthropy’s operating values from day one?”