That 80k guide seems aimed at people who don't yet have any software engineering experience. I'm curious what you think the path is from "Average software engineer with 5+ years experience" to the kind of engineer you're looking for, since that's the point I'm starting from.
Can someone briefly describe what empirical AI safety work Cohere is doing? I hadn't heard of them until this post.
This comment reflects those of me and not my employer (Cohere).
We are currently massively growing our safety team on both engineering and product sides and one of our major bottlenecks is the above technical talent. We are currently heavily focused on making our models in production as safe as possible during training and during production. One of the biggest projects to this extent is the safety harness project which should have more information coming out soon. https://docs.cohere.ai/safety-harness/. We are heavily focused on worse-case scenario's especially as anyone can use our models relatively quickly. Here are 2 of the papers the safety team has worked on in the past. We have much more in the timeline.
I'm an engineer, but the positions seem to tend to require living in specific locations, so I cannot apply.
I'm going to take this blog post as the explanation for the rejection I got from Anthropic five mins ago for the researcher position.
As a self-taught programmer who's dabbled in ML, but has only done front and back-end web work: it's been pretty frustrating trying to find a way to work on ML or AI safety the last four years. I think some of the very recent developments like RR's ML boot camp are promising on this front, but I'm pretty surprised that Redwood was surprised they would get 500 applications. We've been telling people explicitly "this is an emergency" for years now, but tacitly "but you can't do anything about it unless you're a 99th percentile programmer and also positioned in the right place at the right time to apply and live in the bay area." Or, that's how it's felt to me.
I wonder if some subset of the people who weren't accepted to the Redwood thing could organise a remote self-taught version. They note that "the curriculum emphasises collaborative problem solving and pair programming", so I think that the supervision Redwood provides would be helpful but not crucial. Probably the biggest bottleneck here would be someone stepping up to organise it (assuming Redwood would be happy to share their curriculum for this version).
I agree that this would be helpful if Redwood shares their curriculum. If someone is willing to take up lead organizing, I'd be happy to help out as much as I can (and I suspect this would be true for a non-insignificant number of people who applied to the thing). I'd do it myself, but I expect not to have the free time to commit to that and do it right in the next few months.
Same here (Not sure yet if I get accepted to AISC though). But I would be happy with helping or co-organizing something like Richard_Ngo suggested. (Although I've never organized something like that before) Maybe a virtual version in (Continental?) Europe, if there are enough people
Maybe, we could also send out an invitation to all the people who got rejected to join a Slack channel. (I could set that up, if necessary. Since I don't have the emails, though, someone would need to send the invitations). There, based on the curriculum, people could form self-study groups on their own with others close-by (or remotely) and talk about difficulties, bugs, etc. Maybe, even the people who got not rejected could join the slack and help to answer questions (if they like and have time, of course)?
I've created a discord for the people interested in organizing / collaborating / self-study: https://discord.gg/Ckj4BKUChr People could start with the brief curriculum published in this document, until a full curriculum might be available :)
Redwood was surprised they would get 500 applications
I'm curious what this is referring to - was there public communication to that effect?
From Redwood's application update (rejecting those who didn't make the cut):
We had many more applicants than I expected, and even though we expanded the program to have space for 30 participants instead of 20, we aren't able to accept that many of our 500 applicants, including many applicants who seem very promising and competent. I am sad that we don't have space for more people.
Oh, I misread, I thought they would have been surprised to get 500 applicants for an open job position.
This might be a false alarm, but "tell me your thoughts on AI and the future" is an extremely counterproductive interview question. You're presenting it as a litmus test for engineers to apply to themselves, and that's fine as far as it goes. But if it's typical or analogous to some other test(s) you use to actually judge incoming hires, it doesn't bode well. By asking it you are, on some level, filtering for public speaking aptitude and ability to sound impressively thoughtful, two things which probably have little or nothing to do with the work you do.
I realize that might seem like a pedantic point, and you might be asking yourself: "how many smart people who want to work here can't drop impressive speeches about X? We'll just refrain from hiring that edge case population." The reason it's relevant that your interview "could" be selecting for the wrong thing is because recruitment is an adversarial process, not a random process. You are fighting against other technology companies who have better and more scientific hiring pipelines, and more time and money to build them. Those companies often diligently reject the people who can speak well but not code. The result is the candidates you're looking at will almost always seem curiously good at answering these questions, and under-performing on actual workplace tasks. Even if this were happening I'm sure you'd believe everything is fine, because your VC money lets you give enormous salaries that obscure the problem and because AI safety companies get a glut of incoming attention from sites like Lesswrong. All the more reason not to waste those things.
Worse, you have now published that question, so you will now get a large amount of people who coach their answers and practice them in front of a mirror in preparation for the interview. "Oh well, most people are honest, it'll only be like 1/2/5/10/25% of our applicants that..." - again, not necessarily true of your passing applicants, and definitely not necessarily true of applicants rejected or less-well-compensated by your competitors.
You're presenting it as a litmus test for engineers to apply to themselves, and that's fine as far as it goes
I can reassure you that it is in fact a litmus test for engineers to apply to themselves, and that's as far as it goes.
While part of me is keen to discuss our interview design further, I'm afraid you've done a great job of laying out some of the reasons not to!
Any chance that Anthropic might expand the team to remote international collaboration in the future? I would apply but I am from Ukraine. Many great software companies successfully switched to remote work and covid crysis boosted this practice a lot. So just wondering.
It's not impossible, but it appears unlikely for the foreseeable future. We do sponsor visas, but if that doesn't suit then I'd take a look at Cohere.ai, as they're one org I know of with a safety team who are fully-onboard with remote.
Can you add something about whether Anthropic does or doesn't allow remote work to the job listings? I'm infering from the lack of any mention of remote work that in-person is strictly required but I'm not sure if that's what you're intending.
In-person is required. We'll add something to the job descriptions in the new year, thanks for the heads up!
I'm an experienced engineer and EA excited to work on these things, but I am only available part time remote because I am raising my kid, so I'm not applying right now.
If I knew of useful FOSS work that was directly applicable I might be spending time doing it.
EleutherAI has a whole project board dedicated to open-source ML, both replicating published papers and doing new research on safety and interpretability.
(opinions my own, etc)
Thanks, I was aware of Eleuther but I wasn't previously aware how much they cared about alignment-related progress.
I have a background in software engineering but I would like to get into AI safety research.
A problem I have had is that I didn't know whether I should pursue the research scientist or research engineer paths which seem to be quite different. Becoming a research engineer involves lots of work with ML code whereas to become a research engineer you usually have to get a PhD and do some research.
I read in an older document that there was a bottleneck in talent for research scientists and engineers. However, this seems to have changed according to your post and now there seems to be a greater shortage of research engineers than research scientists.
As a result, I am now leaning more in favor of becoming a research engineer. Another advantage is that the research engineer path seems to have a lower barrier to entry.
Top line: 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.
Why engineers?
May last year, OpenAI released GPT-3, a system that did surprisingly well at a surprisingly broad range of tasks. While limited in many important ways, a lot of AI safety folk sat up and noticed. Systems like GPT-3 might not themselves be the existential threat that many of us are worried about, but it's plausible that some of the issues that will be found in such future systems might already be present in GPT-3, and it's plausible to think solving those issues in GPT-3 will help us solve equivalent issues in those future systems that we are worried about.
As such, AI safety has suddenly developed an empirical subfield. While before we could only make predictions about what might go wrong and how we might fix those things, now we can actually run experiments! Experiments are not and should never be the entirety of the field, but it's a new and promising direction that leverages a different skill set to more 'classic' AI safety.
In particular, the different skill set it leverages is engineering. Running experiments on a real - if weak - AI system requires a substantial stack of custom software, with projects running from hundreds of thousands to millions of lines of code. Dealing with these projects is not a skillset that many folks in AI safety had invested in prior to the last 18 months, and it shows in our recruitment.
What kind of engineers?
Looking at the engineers at Anthropic right now, every one of them was a great software engineer prior to joining AI safety. Every one of them is also easy to get on with. Beyond that, common traits are
This is not a requirements list though. Based on the people working here already, 'great software engineer' and 'easy to get on with' are hard requirements, but the things in the list above are very much nice-to-haves, with several folks having just one or none of them.
Right now our job listings are bucketed into 'security engineer', 'infrastructure engineer', 'research engineer' and the like because these are the noun phrases that a lot of the people we like identify themselves with. But what we're actually most concerned about are generally-great software engineers who - ideally - have some extra bit of deep experience that we lack.
How does engineering compare to research?
At Anthropic there is no hard distinction between researchers and engineers. Some other organisations retain the distinction, but the increasing reliance of research on substantial, custom infrastructure is dissolving the boundary at every industrial lab I'm familiar with.
This might be hard to believe. I think the archetypal research-and-engineering organisation is one where the researchers come up with the fun prototypes, and then toss them over the wall to the engineers to clean up and implement. I think the archetype is common enough that it dissuades a lot of engineers from applying to engineering roles, instead applying to research positions where they - when evaluated on a different set of metrics than the ones they're best at - underperform.
What's changed in modern AI safety is that the prototypes now require serious engineering, and so prototyping and experimenting is now an engineering problem from the get-go. A thousand-line nested for-loop does not carry research as far as it once did.
I think this might be a hard sell to folks who have endured those older kinds of research organisations, so here are some anecdotes:
Should I apply?
It's hard to judge sight-unseen whether a specific person would suit AI safety engineering, but a good litmus test is the one given at the top of this post:
Are you already there? Could you get there with a month or two of effort?
I like this as a litmus test because it's very close to what my colleagues and I do all day. If you're a strong enough engineer to make a successful pull request to PyTorch, you're likely a strong enough engineer to make a successful pull request to our internal repos.
Actually, the litmus test above is only one half of the actual litmus test I give folk that I meet out and about. The other half is
with a pass being a nuanced, well-thought-out response.
Should I skill up?
This post is aimed at folks who already can pass the litmus test. I originally intended to pair it with another post on skilling up to the point of being able to pass the test, but that has turned out to be a much more difficult topic than I expected. For now, I'd recommend starting with 80k's software engineering guide.
Take homes
We want more great engineers.
If you could write a pull request for a major ML library, you should apply to one of the groups working on empirical AI safety: Anthropic, DeepMind Safety, OpenAI Safety, Apollo and Redwood Research.
If that's not you but you know one or more great engineers, ask them if they could write a pull request for a major ML library. If yes, tell them to apply to the above groups.
If that's not you but you'd like it to be, watch this space - we're working on skilling up advice.
This post is twinned with the same one on the EA Forum