Meeting Title: Brainforge AI Engineer Interview Date: 2026-04-13 Meeting participants: Kaela Gallagher, Matthew Kulina
WEBVTT
1 00:00:23.970 ⇒ 00:00:25.760 Kaela Gallagher: Hey, Matthew, how’s it going?
2 00:00:25.760 ⇒ 00:00:26.880 Matthew Kulina: Good, how are you?
3 00:00:27.040 ⇒ 00:00:30.040 Kaela Gallagher: Good! Thanks for taking some time for me.
4 00:00:30.040 ⇒ 00:00:31.519 Matthew Kulina: Absolutely, happy Monday.
5 00:00:31.520 ⇒ 00:00:35.740 Kaela Gallagher: Happy Monday. It was Sam that you had connected with, right?
6 00:00:35.740 ⇒ 00:00:36.850 Matthew Kulina: Yes, yeah.
7 00:00:36.850 ⇒ 00:00:40.659 Kaela Gallagher: Okay, and you guys found each other in, like, a Cleveland group or something?
8 00:00:40.660 ⇒ 00:00:47.429 Matthew Kulina: Yeah, yeah, so I grew up in Cleveland. I live in Toledo now, which is just, like, 2 hours west of Cleveland, but I stay in touch.
9 00:00:47.430 ⇒ 00:00:47.810 Kaela Gallagher: Okay.
10 00:00:47.810 ⇒ 00:00:57.560 Matthew Kulina: A lot of that area in general, just because of high school and stuff, right? So, it’s always interesting how, you know, you make connections when you least expect them.
11 00:00:57.830 ⇒ 00:01:02.449 Kaela Gallagher: Yeah, okay, cool. What’s, like, putting you on the market right now?
12 00:01:03.160 ⇒ 00:01:08.720 Matthew Kulina: Kind of just a different change of pace, something new.
13 00:01:08.920 ⇒ 00:01:26.220 Matthew Kulina: Currently in my role at Gambling.com, I kind of got brought on as the first hire for a new project and a new team. And we’ve built that up to about, including me, it’s 10 developers now, and…
14 00:01:26.370 ⇒ 00:01:31.280 Matthew Kulina: the product is so stable, we basically, I think about…
15 00:01:31.410 ⇒ 00:01:45.789 Matthew Kulina: just before Thanksgiving last year, we were able to basically just completely remove our weekend on-call schedule, because we haven’t had anything for, like, 8 months, and we’re like, okay, cool, like, it’s working.
16 00:01:47.600 ⇒ 00:01:55.109 Matthew Kulina: And honestly, I hate… I don’t want to say, like, get hit by a bus, but let’s say if I won the lottery and I could leave, they’d be fine.
17 00:01:55.670 ⇒ 00:01:56.570 Kaela Gallagher: Yeah, okay.
18 00:01:56.680 ⇒ 00:01:59.729 Matthew Kulina: I think it’s time for Dad to leave the nest, is how I would do it.
19 00:01:59.730 ⇒ 00:02:00.190 Kaela Gallagher: describe it.
20 00:02:00.630 ⇒ 00:02:03.310 Kaela Gallagher: Okay, what’s the product that you built there?
21 00:02:03.860 ⇒ 00:02:17.430 Matthew Kulina: So it’s partially kind of like an internal tool system, for what we call partnerships, that Gambling.com kind of partners with, and it’s, you know, SDKs, MCPs,
22 00:02:18.040 ⇒ 00:02:25.509 Matthew Kulina: that can interface with these other products that these companies have. I’m trying to be ambiguous about it.
23 00:02:25.510 ⇒ 00:02:26.280 Kaela Gallagher: Yeah.
24 00:02:27.250 ⇒ 00:02:38.819 Matthew Kulina: But, yeah, it kind of entailed everything from being able to, like, place bets to getting player data, you know, up to, you know, our partners,
25 00:02:39.660 ⇒ 00:02:57.440 Matthew Kulina: you know, geo-targeting based on where the user would be, because you quickly learn that the betting world, you know, every state has its legislation surrounding it, and boy, if you are not in lockstep with that, you get in a lot of trouble. So,
26 00:02:57.460 ⇒ 00:03:02.219 Matthew Kulina: Yeah, just making sure that we are always in compliance with everything going on.
27 00:03:03.080 ⇒ 00:03:07.469 Matthew Kulina: But, yeah, it’s been… been pretty awesome, I’ll say that.
28 00:03:07.660 ⇒ 00:03:14.079 Kaela Gallagher: Okay, okay, cool. And that role, are you going in person for that, or are you remote?
29 00:03:14.080 ⇒ 00:03:19.080 Matthew Kulina: No, I’ve been remote, oh my gosh, almost 6 years now?
30 00:03:20.490 ⇒ 00:03:22.390 Matthew Kulina: Yeah, about 6 years.
31 00:03:23.120 ⇒ 00:03:23.850 Kaela Gallagher: Yeah.
32 00:03:23.850 ⇒ 00:03:30.730 Matthew Kulina: So, yeah, yeah, I love being remote. I think that kind of what kicked it off for me,
33 00:03:30.960 ⇒ 00:03:34.470 Matthew Kulina: Was the pandemic, and ever since, it’s been…
34 00:03:34.710 ⇒ 00:03:38.690 Matthew Kulina: I get more work done, and I get more laundry done, too. So, I can’t argue.
35 00:03:39.150 ⇒ 00:03:57.849 Kaela Gallagher: Yeah, okay, alright, cool. I think the role that you and Sam had connected on was, like, our AI engineer position. I’m curious, like, where your passion for AI comes from, and if you’re kind of working on any AI projects currently.
36 00:03:58.310 ⇒ 00:04:05.830 Matthew Kulina: Yeah, I actually just implemented a, a RAG system internally for us, at gambling, and then…
37 00:04:06.350 ⇒ 00:04:16.939 Matthew Kulina: ever since AI kind of started going, I’ve always messed around with it. I run, like, a little personal AI lab, you know, at home, so I can run, like, local models.
38 00:04:17.480 ⇒ 00:04:28.740 Matthew Kulina: at my leisure, or just have tasks running in the background. It’s… at first, my wife was kind of opposed to it, but now she… she understands the benefit of, you know, being able to just
39 00:04:28.900 ⇒ 00:04:32.569 Matthew Kulina: tell it to do something, and it’ll put it on our grocery list, per se.
40 00:04:32.570 ⇒ 00:04:33.680 Kaela Gallagher: Yeah, yeah.
41 00:04:33.680 ⇒ 00:04:39.899 Matthew Kulina: Or even find us the best deal on a product that she’s interested in. But
42 00:04:40.210 ⇒ 00:04:42.840 Matthew Kulina: I don’t know, it’s an interesting…
43 00:04:44.320 ⇒ 00:04:50.579 Matthew Kulina: tool, because to me, there’s never been such an all-encompassing technology that affects everybody, right?
44 00:04:51.120 ⇒ 00:05:07.139 Matthew Kulina: There’s been automation, let’s say in the automotive industry, you know, with, like, robotics, per se, right? Or robotics could have… in any, like, mechanical factory foundation, right? It probably drastically changed that… that whole…
45 00:05:07.330 ⇒ 00:05:10.739 Matthew Kulina: job that people had, right?
46 00:05:11.050 ⇒ 00:05:18.209 Matthew Kulina: And I think this technology, at least, it affects everything and anything, right?
47 00:05:18.760 ⇒ 00:05:31.719 Matthew Kulina: and probably the good… there’s great tools for it, and there’s uses for it that I really don’t advocate or don’t like, right? Like military stuff. But for me, it’s kind of…
48 00:05:32.110 ⇒ 00:05:38.479 Matthew Kulina: I think it’s… Something that can help humanity overall, in a great way.
49 00:05:38.730 ⇒ 00:05:54.119 Kaela Gallagher: Yeah, I think that’s… that’s fair. We’re trying to use it in that helpful way as well with our… our clients. We basically offer, like, 3 service lines to clients. One is our data team, one’s AI, and then one is strategy and analytics, and…
50 00:05:54.140 ⇒ 00:05:59.729 Kaela Gallagher: Like, oftentimes we kind of have a mix of… of those supporting our clients, but…
51 00:06:00.070 ⇒ 00:06:15.330 Kaela Gallagher: Yeah, we work with, like, mostly small to mid-sized companies, like healthcare, SaaS, CPG. We work with a couple brands that are, like, on the shelves at Target and Walmart, so you might recognize them, but,
52 00:06:15.330 ⇒ 00:06:21.670 Kaela Gallagher: in this role, in our AI engineer role, you’d be working with probably, like, 2 to 3 at a time, so…
53 00:06:21.700 ⇒ 00:06:38.569 Kaela Gallagher: For us, it’s super important that our engineers can contact switch, because you’re gonna have multiple projects within the day, and also have, like, strong communication skills. We’re about 25 people right now as a company, and so our engineers are still joining calls with clients.
54 00:06:38.630 ⇒ 00:06:52.880 Kaela Gallagher: So being able to, like, discuss technical things in a way that makes sense is super important, too. So, yeah, that’s kind of an overview of the role. Right now, our entire team is on a 1099 basis, so.
55 00:06:52.880 ⇒ 00:06:53.220 Matthew Kulina: Okay.
56 00:06:53.220 ⇒ 00:06:57.970 Kaela Gallagher: This role would start off as an independent contracting position.
57 00:06:58.090 ⇒ 00:06:59.340 Kaela Gallagher: But…
58 00:06:59.720 ⇒ 00:07:16.720 Kaela Gallagher: We are looking to start converting our team to W-2. I would say, probably within the year 2026 is the goal, but just want to preface, like, the 1099, is how we’d start off. So, any questions about any of that?
59 00:07:17.660 ⇒ 00:07:24.980 Matthew Kulina: Not necessarily. I think, it’ll be awesome to… like, I’ve always been in, you know.
60 00:07:25.300 ⇒ 00:07:41.109 Matthew Kulina: calls with clients or, you know, customers is however you perceive it. Yeah. And at least in my… even my last role, which was iMark, it’s like a digital agency, and that’s where I’ve been able to work with, like, JetBlue, New York Times, you know, Red Bull, and stuff like that, so…
61 00:07:41.250 ⇒ 00:07:50.139 Matthew Kulina: And even in my current role, I don’t know if you know of who Gannett is, they basically own, like, any website about news ever.
62 00:07:50.640 ⇒ 00:07:53.210 Matthew Kulina: They own USA Today. Okay.
63 00:07:53.490 ⇒ 00:08:05.720 Matthew Kulina: So, they basically own, like, any regional, small newspaper, probably, as well. It’s just, like, a huge, huge company, and I’ve sat in meetings with the CEO for…
64 00:08:05.900 ⇒ 00:08:06.740 Matthew Kulina: you know.
65 00:08:07.260 ⇒ 00:08:24.119 Matthew Kulina: projections and planning, that we wanted to do in lockstep with them. So, yeah, I don’t know. I’ve met plenty of engineers that are amazing engineers, and then I’ve also met a lot of engineers that can barely talk to a bar of soap if they had to, so…
66 00:08:24.920 ⇒ 00:08:36.709 Kaela Gallagher: Yeah, yeah, we’re looking for, the unicorn, for, like, lack of better words, you know? The engineer, that’s also a great client-facing person, so…
67 00:08:36.990 ⇒ 00:08:48.439 Kaela Gallagher: I know you mentioned using AI within work and home. I’m curious, like, what kind of, like, agents or frameworks you have been using.
68 00:08:48.720 ⇒ 00:08:53.730 Matthew Kulina: So, mostly for work, we obviously have used, like, OpenAI or,
69 00:08:55.410 ⇒ 00:09:06.760 Matthew Kulina: Anthropic, you know, Cloud Code, stuff like that. You know, we’ve used some of, OpenAI’s, you know, RAG processes that they provide.
70 00:09:07.060 ⇒ 00:09:17.790 Matthew Kulina: with agentic modeling, mostly related to that, but, like, locally, it’s been just, like, VLLM, OpenClaw, I started running that a few months ago,
71 00:09:18.440 ⇒ 00:09:24.040 Matthew Kulina: I’m big on privacy, so, like, a lot of my personal stuff just runs on local hardware instead.
72 00:09:24.040 ⇒ 00:09:24.640 Kaela Gallagher: Hmm.
73 00:09:25.120 ⇒ 00:09:31.309 Matthew Kulina: Because, you know, when I, like, pass it a tax form, I don’t want it to be like, yeah, here’s a SOATH, right?
74 00:09:31.310 ⇒ 00:09:32.010 Kaela Gallagher: Yeah.
75 00:09:32.010 ⇒ 00:09:45.000 Matthew Kulina: So, you know, there’s a lot of nuance to the tools, and you kind of have to at least be aware of what could happen, but if you lock it down well enough, you know, for local stuff, it’s really, really great.
76 00:09:45.470 ⇒ 00:09:59.640 Matthew Kulina: Yeah, most of it’s been… at least, like, I pay for the Anthropic Max subscription that’s, like, 200 bucks a month on top of, you know, running it locally as well, for certain things, just because of…
77 00:10:00.370 ⇒ 00:10:07.499 Matthew Kulina: again, you know, I’ve basically built, like, an interface for my family to be able to, like, tap in and use it, right?
78 00:10:07.950 ⇒ 00:10:19.100 Matthew Kulina: and it kind of kicks off of the description that I’m paying for, but it lets, you know, anybody in our family to kind of, like, ask questions or have access to this in, like,
79 00:10:19.610 ⇒ 00:10:34.250 Matthew Kulina: I basically gave them two gateways. I was like, here’s the very private one, you know, it might not be as smart as what this one can do, but, you know, if you just need to crunch numbers or do something… nothing particular, use the one I pay for. If it’s, like.
80 00:10:34.440 ⇒ 00:10:42.620 Matthew Kulina: Checking photos, or, you know, something… You know, looking at something private, use this one.
81 00:10:42.820 ⇒ 00:10:53.139 Kaela Gallagher: Yeah, yeah, okay, that makes sense. One last, like, logistics question for you. What’s the, like, total comp range you’d be looking for to make a move?
82 00:10:53.140 ⇒ 00:10:58.660 Matthew Kulina: Like, with 1099, or with W2, potentially?
83 00:10:59.080 ⇒ 00:11:03.519 Kaela Gallagher: I guess, like, 1099 would be helpful to start.
84 00:11:03.520 ⇒ 00:11:09.190 Matthew Kulina: I’d probably be at, like, around 10.99.
85 00:11:10.140 ⇒ 00:11:13.799 Matthew Kulina: Because taxes hit pretty frickin’ hard, at least in Ohio.
86 00:11:13.970 ⇒ 00:11:16.550 Matthew Kulina: For 1099.
87 00:11:17.040 ⇒ 00:11:24.899 Matthew Kulina: I’m open to, like, you know, back and forth on that. I know how that goes at times, but at least with my experience.
88 00:11:25.130 ⇒ 00:11:32.040 Matthew Kulina: My ability to at least be cordial with clients and, be… have a pretty cool, chill head,
89 00:11:32.450 ⇒ 00:11:34.359 Matthew Kulina: I think goes a long way as well.
90 00:11:34.530 ⇒ 00:11:44.969 Kaela Gallagher: Yeah, yeah, agreed. Our typical range for this role that we’re kind of looking in is closer to, like, 80 to 90 an hour, so…
91 00:11:44.980 ⇒ 00:12:01.429 Kaela Gallagher: Let me get with the team, and before, like, moving you forward to next rounds, would want to make sure we can get at least a little bit closer to the 125 mark, and then if so, I can send over an email to, like, book some time with the first interviewer.
92 00:12:01.700 ⇒ 00:12:15.349 Matthew Kulina: Yeah, that’d be great. Again, yeah, I’m flexible, you know, based on it, there’s a lot of things that can come up, or come down, right? For, like… because to me, like, you basically throw 30%, and that’s what a W-2 ends up being.
93 00:12:16.210 ⇒ 00:12:21.679 Matthew Kulina: But again, yeah, open a conversation about it, and go back and forth, and…
94 00:12:21.900 ⇒ 00:12:23.150 Matthew Kulina: Hopefully it’s a good fit.
95 00:12:23.470 ⇒ 00:12:33.520 Kaela Gallagher: Cool. Cool. Yeah, and just for, a heads up, what the interview process would look like is it’s 3 rounds, so the first one is just overall experience.
96 00:12:33.520 ⇒ 00:12:44.999 Kaela Gallagher: Second, it gets a little bit more technical. There’s no, like, live coding or anything, though. And then the third one, we’d give you, like, a take-home challenge, and you’d come to a final panel with… with your solution to present.
97 00:12:45.000 ⇒ 00:12:47.210 Matthew Kulina: Sure. Sounds good.
98 00:12:47.420 ⇒ 00:12:51.010 Kaela Gallagher: Cool. Well, awesome. Thanks so much for your time today, it was great getting to know you.
99 00:12:51.010 ⇒ 00:12:54.369 Matthew Kulina: Thank you, you as well. Where are you based out of?
100 00:12:54.540 ⇒ 00:12:55.729 Kaela Gallagher: I’m in LA.
101 00:12:55.730 ⇒ 00:12:56.610 Matthew Kulina: Oh.
102 00:12:56.610 ⇒ 00:12:57.330 Kaela Gallagher: Yeah.
103 00:12:58.290 ⇒ 00:13:01.639 Matthew Kulina: Your weather’s way better than mine, so…
104 00:13:01.640 ⇒ 00:13:09.119 Kaela Gallagher: We are… we are lucky with that, yeah. And I’m… I’m also really lucky, because there’s, like, 4 other Brainforge people that live within a 10.
105 00:13:09.120 ⇒ 00:13:09.690 Matthew Kulina: Really.
106 00:13:09.690 ⇒ 00:13:12.759 Kaela Gallagher: of me, so we get together, like, once a week, so it’s really fun.
107 00:13:12.760 ⇒ 00:13:18.010 Matthew Kulina: That’s super cool. I visited friends in LA, what, 4 years ago now?
108 00:13:18.010 ⇒ 00:13:18.570 Kaela Gallagher: Okay.
109 00:13:18.570 ⇒ 00:13:22.090 Matthew Kulina: Hollywood’s, you know, sign and everything, and that was awesome, so…
110 00:13:22.220 ⇒ 00:13:23.060 Kaela Gallagher: Cool!
111 00:13:23.060 ⇒ 00:13:26.400 Matthew Kulina: very jealous of at least the food, oh my gosh.
112 00:13:26.400 ⇒ 00:13:27.030 Kaela Gallagher: I agree.
113 00:13:27.030 ⇒ 00:13:28.510 Matthew Kulina: I would die happy.
114 00:13:28.510 ⇒ 00:13:32.260 Kaela Gallagher: The food is phenomenal, yes.
115 00:13:32.260 ⇒ 00:13:35.350 Matthew Kulina: Well, again, thank you for your time as well.
116 00:13:35.480 ⇒ 00:13:37.040 Matthew Kulina: Hope you have a wonderful day.
117 00:13:37.040 ⇒ 00:13:40.770 Kaela Gallagher: Thank you, you too. Alrighty. Bye.