How To Hire AI Engineers — with James Brady & Adam Wiggins of Elicit

**SPEAKER_2** (0:07)
Okay, so welcome to the Latent Space Podcast. This is another remote episode that we're recording. This is the first one that we're doing around a guest post, and I'm very honored to have two of the authors of the post with me, James and Adam from Elicit. Welcome, James, welcome, Adam.

**James Brady** (0:22)
Thank you, great to be here.

**Adam Wiggins** (0:23)
Hey there.

**SPEAKER_2** (0:24)
Okay, so I think I will do this kind of in order. I think James, you're sort of the primary author.
So James, you are head of engineering at Elicit. You also were VP engine at Teespring, and Spring as well. And you also, you have a long history in sort of engineering. How did you find your way into something like Elicit where you are basically traditional sort of VP and VP technology type person moving into more of an AI role?

**James Brady** (0:52)
Yeah, that's right. It definitely was something of a sideways move, if not a left turn. So the story there was, I'd been doing, as you said, VP technology CTO type stuff for around about 15 years or so.
And noticed that there was this crazy explosion of capability and interesting stuff happening within AI and ML and language models, that kind of thing.
I guess this was in 2019 or so. I decided that I needed to get involved. This is a kind of generational shift and spent maybe a year or so trying to get up to speed on the state of the art, reading papers, reading books, practicing things, that kind of stuff. Was going to found a startup actually in the space of interpretability and transparency. And through that met Andreas, who has obviously been on the podcast before, asked him to be an advisor for my startup. And he countered with maybe you'd like to come and run the engineering team at Elicit, which it turns out was a much better idea. And yeah, I kind of quickly changed in that direction. So I think some of the stuff that we're going to be talking about today is how actually a lot of the work when you're building applications with AI and ML looks and smells and feels much more like conventional software engineering with a few key differences rather than really deep ML stuff. And I think that's one of the reasons why I was able to transfer skills over from one place to the other.

**SPEAKER_2** (2:12)
Yeah, I definitely agree with that. I do often say that I think AI engineering is about 90% software engineering with the 10% of really strong, really differentiated AI engineering. And that obviously, that number might change over time.
I want to also welcome Adam onto my podcast because you welcomed me onto your podcast two years ago.

**Adam Wiggins** (2:31)
Yeah, that was a wonderful episode.

**SPEAKER_2** (2:32)
That was a fun episode. You famously founded Heroku. You just wrapped up a few years working on Muse.
And now you've described yourself as a journalist, internal journalist working on Elicit.

**Adam Wiggins** (2:43)
Yeah, well, I'm kind of a little bit of in a wandering phase here and trying to take this time in between ventures to see what's out there in the world. And some of my wandering took me to the Elicit team and found that they were some of the folks who were doing the most interesting, really deep work in terms of taking the capabilities of language models and applying them to what I feel like are really important problems.
So in this case, science and literature search and that sort of thing. It fits into my general interest in tools and productivity software. I think it is a tool for thought in many ways, but a tool for science, obviously, if we can accelerate that discovery of new medicines and things like that, that's just so powerful.
But to me, it's kind of also an opportunity to learn at the feet of some real masters in this space, people who have been working on it since it was, before it was cool, if you wanna put it that way. So for me, the last couple of months have been this crash course, and why I sometimes describe myself as an internal journalist is I'm helping to write some posts, including supporting James in this article here we're doing for Late in Space, where I'm just bringing kind of my writing skill and that sort of thing to bear on their very deep domain expertise around language models and applying them to the real world and kind of surface that in a way that's, I don't know, accessible, legible, that sort of thing. And so, and the great benefit to me is I get to learn this stuff in a way that I don't think I would or I haven't just kind of tinkering with my own side projects.