Shawn Wang: prediction

Transcript context

…Yeah, a couple of thoughts here. So firstly, when I think back to how I was when I was in my early 20s, when I was at, when I was at college or university, the maturity and capabilities and just kind of general put togetherness of people at that age now is strikingly different to, to, to where I was then. And I, I think this is. Not because I was especially lexadesical or something when I was, when I was young. I think it's I hear the same thing echoed in other people about my, about my age. So the takeaway from that is finding a way of presenting yourself to and identifying and bringing in really high capability young people into your organization. I mean, it's always been true, but I think it's even more true now. They're kind of more professional, more capable, more committed more driven. have more of a sense of what they're all about than certainly I did 20 years ago. So that's, that's the first thing. I think the second thing is in terms of the interview process, this is somewhat a general take, but it definitely applies to AI engineer roles. And I think more so to AI engineer roles. I really have a strong dislike and distaste for interview questions, which are arbitrary and kind of strip away all the context from what it really is to do the work. We try to make the interview process that's illicit. A simulation of working together. The only people that we go into an interview process with. are pretty obviously extraordinary really, really capable. They must have done something for them to have moved into the proper interview process. So it is a check on technical capability and in the ways that we've described, but it's at least as much them sizing us up. Like, is this something which is worth my time? Is it something that I'm going to really be able to dedicate myself to? So being able to show them, this is really what it's like working at Elicit. This is the people you're going to work with. These are the kinds of tasks that you're going to be doing. This is the sort of environment that we work in. These are the tools we use. All that kind of stuff is really, really important from a candidate experience, but it also gives us a ton more signal as well about, , what is it actually like to work with this person? Not just can they do really well on some kind of leak code style, style problem. I think the reason that it bears a particularly on the AI engineer role is because it is something of an emerging category, if you will. So there isn't a very kind of. Well established do these that nobody's written the book yet Maybe this is the beginning of us writing the book and how to get hired as an AI engineer but that book doesn't exist at the moment and Yeah, It's an empirical job as, as much as any other kind of software engineering. It's, it's less about having kind of book learning and more about being able to apply that in a real world situation. So let's make the interview as close to a real world situation as possible. I do, I do co sign a lot of that. Yeah, I think this is a really great overview of just the, the, the sort of state of, Hiring AI engineers. And I honestly, that's just what, what AI engineering even is, which it really is like, when I was thinking about this as an industrial movement it was very much around, around the labor market, actually and the economic forces that give rise to, to a role like this both on the incentives of the model labs, as well as the demand and supply of engineers and the interest level of companies And the engineers working on these problems. So I definitely see you guys as pioneers. Thank you so much for putting together this piece, which is something I've been seeking for a long time. You even shared your job description, your reading list, and your interview loop. So, , if anyone's looking to hire AI engineers, I expect this to be the definitive piece and definitive podcast covering it. So thank you so much for taking the time to do this. It was fun. Thanks for having us. Thanks a…

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