Meeting Title: Brainforge AI Engineer Interview Date: 2026-05-06 Meeting participants: Kaela Gallagher, Mark
WEBVTT
1 00:04:29.000 ⇒ 00:04:37.399 Mark: Kayla, I’m so sorry I’m late. I, I got into a meeting with my boss, and I couldn’t tell him I was going to a job interview, so…
2 00:04:37.400 ⇒ 00:04:44.240 Kaela Gallagher: Of course! No, no worries at all. Thanks for, taking some time for me today, I appreciate it.
3 00:04:44.350 ⇒ 00:04:46.970 Mark: Of course, yeah.
4 00:04:47.540 ⇒ 00:04:59.860 Kaela Gallagher: Yeah, excited to learn a little bit more about you, and I can kind of tell you more about Brainforge and what we’re doing as well, but the reason I came across your profile is,
5 00:04:59.860 ⇒ 00:05:15.679 Kaela Gallagher: We are growing our team, especially on the AI engineering side, and we’re fully remote, we have team members across the globe, but our CEO is based in Austin, so that’s definitely, like, a preference for us in terms of location.
6 00:05:16.140 ⇒ 00:05:25.300 Kaela Gallagher: So, yeah, noticed that you’re based in Austin, noticed that you did the Gauntlet AI Fellowship, and so, yeah, just curious to know a little bit more.
7 00:05:25.890 ⇒ 00:05:40.210 Mark: Okay, yeah, I did do Gauntlet after about 6 years as a software engineer, where I focused mainly on, like, React Native mobile front ends, so definitely much stronger on the front end, but then…
8 00:05:40.210 ⇒ 00:05:52.629 Mark: you know, going through Gauntlet, I built a ton of AI implementations, just kind of of all types, and now I’m at the Department of the Treasury, and kind of continuing to…
9 00:05:53.070 ⇒ 00:06:04.550 Mark: use AI tools to develop, you know, do traditional development really fast, but also building additional AI implementations. I think I mentioned in my application
10 00:06:04.810 ⇒ 00:06:07.340 Mark: that I… I just pitched, like, a…
11 00:06:07.750 ⇒ 00:06:19.709 Mark: Kind of a tool where we upload transcripts for all the different stand-ups we’ve had, where, for example, our team lead would talk about his discussions with our, you know, customers.
12 00:06:19.950 ⇒ 00:06:22.960 Mark: And those transcripts kind of…
13 00:06:23.220 ⇒ 00:06:41.669 Mark: have a lot of insights about product direction, you know, how the customer’s going to use this product, things like that. I ingested… ingested that to get, like, structured insights, and now I… I talk to Codex, my AI development tool, and when I’m planning a new feature, I can be like, what
14 00:06:41.670 ⇒ 00:06:53.940 Mark: you know, what is this feature trying to accomplish? How will customers use this bit? And it can point to, like, direct evidence from these transcripts. I’m pretty happy with it. So far, I think I’m the only person using it, but…
15 00:06:54.080 ⇒ 00:06:56.320 Mark: I don’t know, I want to tell people to get on it.
16 00:06:56.570 ⇒ 00:07:14.550 Kaela Gallagher: That’s actually really crazy that you mention that, because we have kind of a similar tool that we’ve built internally at Brainforge, so every call that anybody has on Zoom, so this call, for example, the video and the transcripts are stored
17 00:07:14.670 ⇒ 00:07:17.640 Kaela Gallagher: In a big, repository, and…
18 00:07:17.710 ⇒ 00:07:39.450 Kaela Gallagher: we can all go in, and you can view your own calls, you can view calls relating to certain clients that we have, you can click into the call, get a quick Slack summary, or an email summary, or there’s a little AI chatbot, you can ask it questions about the meeting. So we’ve kind of built something, like, similar internally here as well. So, super cool you mentioned that.
19 00:07:43.050 ⇒ 00:07:54.370 Kaela Gallagher: Cool, okay. And then, so right now, Department of the Treasury, you mentioned you’re using Codex. I’m curious, like, what other AI tools, you typically use.
20 00:07:55.070 ⇒ 00:07:59.440 Mark: For the past few months before the Treasury, I had been using Claude.
21 00:07:59.790 ⇒ 00:08:00.580 Kaela Gallagher: Okay.
22 00:08:00.580 ⇒ 00:08:03.290 Mark: So far, I think I would prefer Claude.
23 00:08:03.610 ⇒ 00:08:09.630 Mark: But, you know, the federal government isn’t using it, basically.
24 00:08:09.940 ⇒ 00:08:13.750 Mark: Other tools I’ve used, Manus is really good.
25 00:08:14.340 ⇒ 00:08:21.080 Mark: Oh, there’s one other… There’s a variety of tools. I use a lot of InnateN, as well.
26 00:08:21.530 ⇒ 00:08:22.780 Mark: I’m trying to think…
27 00:08:24.160 ⇒ 00:08:29.350 Mark: I’ve experimented with a lot of things, but I think that’s all I can come up with right now, sorry.
28 00:08:29.560 ⇒ 00:08:40.420 Kaela Gallagher: Okay, yeah, no, no worries. I know you’ve only been with the Department of the Treasury for a couple months, so curious what has you maybe keeping an eye out for something new?
29 00:08:40.820 ⇒ 00:08:42.059 Mark: Oh,
30 00:08:42.559 ⇒ 00:08:50.089 Mark: I just… yeah, I don’t know if I’m ready to jump ship quite yet, but I want to keep, like.
31 00:08:51.240 ⇒ 00:08:58.660 Mark: knowledgeable about what’s going on in the industry, what people are looking for, and you know, maybe I would jump ship. We’ll see.
32 00:08:58.890 ⇒ 00:09:08.450 Kaela Gallagher: Yeah, okay. Okay, cool. I guess, like, what kind of, like, what would you need an opportunity to look like for you to be excited to make a move?
33 00:09:09.330 ⇒ 00:09:12.819 Mark: I like a lot of autonomy.
34 00:09:13.320 ⇒ 00:09:27.389 Mark: I feel like the issue that would hold me back is, like, job security, because, you know, I’m probably one of few people at the Treasury that actually has job security. Like, out in the private sector, it’s… it’s pretty rough.
35 00:09:28.030 ⇒ 00:09:32.499 Mark: So that’s something that I would be kind of more sensitive about.
36 00:09:33.240 ⇒ 00:09:37.770 Mark: And that’s what kind of, you know, discussing during an interview is for.
37 00:09:38.210 ⇒ 00:09:44.259 Kaela Gallagher: Yeah, okay, okay. And then what about pay? Like, what’s the compensation range you’d be targeting?
38 00:09:44.270 ⇒ 00:09:47.279 Mark: Probably, like, 180 plus.
39 00:09:47.840 ⇒ 00:09:48.590 Kaela Gallagher: Okay.
40 00:09:48.760 ⇒ 00:09:56.930 Kaela Gallagher: Okay, got it. I will say, like, with where we’re at right now,
41 00:09:57.030 ⇒ 00:10:04.030 Kaela Gallagher: We are targeting kind of closer to, like, the 120 to 140 range.
42 00:10:04.750 ⇒ 00:10:18.829 Kaela Gallagher: So I don’t think, like, we can meet that pay expectation currently. However, I joined the company a couple months ago, and I’ve already hired 10 people, like, we’re growing really quickly. So…
43 00:10:18.830 ⇒ 00:10:34.539 Kaela Gallagher: I… I see us having, like, a senior AI-related opening where we could meet that pay very soon, so I’d love to stay in touch, and, like, as soon as I have something I think could align with that pay range, yeah, I’d love to let you know.
44 00:10:34.710 ⇒ 00:10:46.259 Mark: Okay, yeah, that sounds good. Can I ask, like, what… what are the people… the people in the 120 to 40 range? What do they look like based on experience? What makes them AI engineers?
45 00:10:46.900 ⇒ 00:11:02.570 Kaela Gallagher: Yeah, so most of them have had, you know, probably their most recent role with an AI engineer title, but typically, I would say they’re more so on the junior side in terms of, like, total years of experience.
46 00:11:02.860 ⇒ 00:11:03.770 Kaela Gallagher: I think…
47 00:11:04.060 ⇒ 00:11:11.779 Kaela Gallagher: something that’s really important to us is, you know, even if they’ve only been in an AI engineer role for
48 00:11:12.090 ⇒ 00:11:25.010 Kaela Gallagher: I don’t know, 6 months or a year, they’re builders, and they’re, like, building things on the side, and they’re, you know, really passionate about it, and maybe they’ve done a program like Gauntlet AI, and, something like that, so…
49 00:11:25.150 ⇒ 00:11:42.239 Kaela Gallagher: I feel like we… we can kind of develop a lot of skills, there’s a lot of opportunity to grow. Because we’re a consultancy, people are supporting multiple clients at once, so I feel like you get 5 years of experience in a year here. So, yeah, with that being said, like, I feel like…
50 00:11:42.240 ⇒ 00:11:49.190 Kaela Gallagher: A lot of the people we’re kind of chatting with, even though they’re on the more junior side, like, we do really see them growing with the organization.
51 00:11:49.750 ⇒ 00:11:53.530 Mark: Okay, that’s cool. Yeah, so it’s like… kind of higher…
52 00:11:53.760 ⇒ 00:11:58.269 Mark: newbies and see who makes it, basically. That’s what you guys are up to.
53 00:11:58.600 ⇒ 00:12:05.720 Kaela Gallagher: Well, I mean, I don’t want to say they’re newbies, like, they might have 5 to 10 years of experience, you know?
54 00:12:05.720 ⇒ 00:12:06.380 Mark: Yeah.
55 00:12:06.550 ⇒ 00:12:15.200 Kaela Gallagher: So, yeah, they’re, for the most part, like, not, you know, fresh out of school with 6 months of experience or anything like that.
56 00:12:15.860 ⇒ 00:12:17.520 Mark: Okay,
57 00:12:17.960 ⇒ 00:12:32.359 Mark: Well, that sounds cool. I… yeah, if… if things change, and you guys are targeting, like, senior, more senior, well, I don’t actually have more than 5 to 10 years experience, though. I’m, like, around 6 years, so I don’t know if you would be interested in me.
58 00:12:32.570 ⇒ 00:12:46.159 Kaela Gallagher: Yeah, maybe senior in terms of, like, pay range is a better way to say it. But yeah, like, just to tell you a little bit more, kind of, about what we’re doing, we’re a data and AI consultancy.
59 00:12:46.160 ⇒ 00:13:01.120 Kaela Gallagher: So, we offer both, like, data and AI services to companies in a wide range of industries. We say small to mid-sized companies, but, we work with brands that are, like, on the shelf at Target and Walmart that you would probably recognize.
60 00:13:02.670 ⇒ 00:13:18.769 Kaela Gallagher: And yeah, fully, fully remote, although Austin is a preference just for the occasional meetup, but, just a lot of, like, flexibility that we’re, we’re able to offer, and, like I said, like, growing really quickly. So, exciting.
61 00:13:18.950 ⇒ 00:13:20.780 Mark: Okay, that’s cool. Yeah,
62 00:13:21.130 ⇒ 00:13:25.519 Mark: That could work out. I look forward to hearing from you again in the future, then.
63 00:13:25.520 ⇒ 00:13:30.640 Kaela Gallagher: Okay, awesome. Well, thank you so much today for your time. It was great getting to know you.
64 00:13:30.640 ⇒ 00:13:32.599 Mark: Yeah, thank you. Thanks for… nice talking to you.
65 00:13:32.600 ⇒ 00:13:34.529 Kaela Gallagher: You too. Take care, Mark.