Meeting Title: Brainforge Recruitment Chat with Sam Date: 2026-05-08 Meeting participants: Sam Kartiganer, Kaela Gallagher
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1 00:01:30.740 ⇒ 00:01:32.680 Kaela Gallagher: Hey Sam, how’s it going?
2 00:01:33.080 ⇒ 00:01:34.270 Sam Kartiganer: Good, how are you?
3 00:01:34.620 ⇒ 00:01:37.909 Kaela Gallagher: Good, thanks for taking some time for me.
4 00:01:38.010 ⇒ 00:01:40.160 Kaela Gallagher: Happy Friday.
5 00:01:40.580 ⇒ 00:01:45.720 Kaela Gallagher: Davis had introduced us, right? How do you know Davis?
6 00:01:45.720 ⇒ 00:01:48.840 Sam Kartiganer: I know him from a, like…
7 00:01:48.970 ⇒ 00:01:59.880 Sam Kartiganer: A couple friends, pretty much. Okay. I met him from, like, a friend a while ago, I think he lived with him for a short period of time, and then I became friends with him kind of after that.
8 00:02:00.390 ⇒ 00:02:11.290 Kaela Gallagher: Okay, okay, so you’re based in Austin as well, then? Yeah. Okay, okay, awesome. I think he mentioned that you were kind of looking for something new, and…
9 00:02:11.290 ⇒ 00:02:22.299 Kaela Gallagher: At Brainforge, we’re always, you know, on the hunt for good talent, so, just thought we might connect. Curious, like, what is putting you on the market right now?
10 00:02:22.300 ⇒ 00:02:25.730 Sam Kartiganer: Awesome. I’m about to finish my master’s, that’s why. I’m a…
11 00:02:25.730 ⇒ 00:02:26.530 Kaela Gallagher: Okay.
12 00:02:26.530 ⇒ 00:02:30.359 Sam Kartiganer: Doing my master’s in data science and machine learning at USC.
13 00:02:30.690 ⇒ 00:02:35.869 Sam Kartiganer: And I’m… I think I’m about to finish in, like…
14 00:02:36.480 ⇒ 00:02:46.399 Sam Kartiganer: In December, so I’m looking for new… I’ve been just now recently going back and, like, applying to a bunch of jobs and stuff, and Davis said that…
15 00:02:46.900 ⇒ 00:02:55.330 Sam Kartiganer: he… I guess he seemed excited about this and said he could, you know, see if I can get into the right… talk to the right people.
16 00:02:55.550 ⇒ 00:03:00.880 Kaela Gallagher: Okay, okay. I also went to USC.
17 00:03:01.110 ⇒ 00:03:08.629 Kaela Gallagher: Curious, like, what kind of roles you have been looking into, like, what you’d be most interested in doing.
18 00:03:08.970 ⇒ 00:03:12.379 Sam Kartiganer: I’ve been looking into mainly,
19 00:03:12.550 ⇒ 00:03:24.589 Sam Kartiganer: like, data science focused. I’ve been looking at data engineering, too. I don’t know if you saw my resume, but I was a data engineer in the past. Before I started my master’s, I worked at another small startup.
20 00:03:24.760 ⇒ 00:03:30.529 Sam Kartiganer: And I was kind of doing, like, DevOps, data engineering, and, like, a small amount of data science for them.
21 00:03:30.750 ⇒ 00:03:33.740 Sam Kartiganer: I collected, like.
22 00:03:34.380 ⇒ 00:03:47.420 Sam Kartiganer: Their whole thing was, collecting YouTube channel data over time and selling it to advertisers, so, I was the one in charge of migrating a database and, getting everything set up there.
23 00:03:47.770 ⇒ 00:03:49.370 Kaela Gallagher: Oh, cool, okay.
24 00:03:49.540 ⇒ 00:04:00.500 Kaela Gallagher: With your graduation date in December, are you looking for, like, part-time work currently, or are you looking for full-time work in December? Like, what does that kind of…
25 00:04:00.500 ⇒ 00:04:14.170 Sam Kartiganer: I can look at full-time work now. The courses are online at the moment, and they also work with jobs with the master’s program. A lot of the people are working full-time while they are pursuing their master’s.
26 00:04:14.330 ⇒ 00:04:18.589 Sam Kartiganer: So it’s… Not… not a big deal for me.
27 00:04:18.980 ⇒ 00:04:22.369 Kaela Gallagher: Okay, okay, cool.
28 00:04:22.820 ⇒ 00:04:37.819 Kaela Gallagher: I guess just to tell you, like, a little bit more about Brainforge and what we do, we are a data and AI consultancy, and I feel like by the day, we’re focusing more and more on the AI piece in terms of the clients we’re working with.
29 00:04:37.820 ⇒ 00:04:43.610 Kaela Gallagher: We work with a lot of, like, small to mid-sized organizations across industries, but…
30 00:04:43.820 ⇒ 00:04:55.770 Kaela Gallagher: there’s, like, a couple clients we work with that are, like, on the shelves at Target and Walmart that you might recognize, so I would consider those maybe even large-sized organizations, but,
31 00:04:55.940 ⇒ 00:05:15.889 Kaela Gallagher: In terms of what we look for on the engineering side, whether you’re, you know, data engineer, analytics engineer, AI engineer, like, utilizing AI in everything you do is super important to us. We’re very AI-forward and invest a lot in our, like, internal tooling there.
32 00:05:15.890 ⇒ 00:05:33.529 Kaela Gallagher: And all of our engineers are still, working directly with clients. We’re about 25 people right now, so we need, our engineering team to be able to communicate very, like, technical ideas in ways that make sense to all of our stakeholders.
33 00:05:33.690 ⇒ 00:05:38.710 Kaela Gallagher: So that’s kind of, like, the most important things we’re looking for right now.
34 00:05:38.970 ⇒ 00:05:46.499 Kaela Gallagher: I will say, we just extended offers in both, like, the data engineering and AI engineering roles.
35 00:05:46.730 ⇒ 00:05:56.900 Kaela Gallagher: So in terms of the next ones we’ll be hiring for, we’re probably not going to be in, like, a super huge rush there, but still willing to interview.
36 00:05:57.300 ⇒ 00:06:12.809 Kaela Gallagher: And… yeah, we’re fully remote, but there’s a good portion of the team in Austin and LA. Those are kind of our hubs right now, so anybody that’s located there or willing to relocate there, that’s definitely a perk for us.
37 00:06:14.590 ⇒ 00:06:19.410 Kaela Gallagher: That is all I have for you. Any questions about us, or the kind of work we do?
38 00:06:19.640 ⇒ 00:06:24.870 Sam Kartiganer: Yeah, I mean, I have a couple questions. I was… Kinda curious how many…
39 00:06:25.300 ⇒ 00:06:29.699 Sam Kartiganer: like, clients are you working with right now? How many, like.
40 00:06:30.060 ⇒ 00:06:33.600 Sam Kartiganer: People are in communication with you guys.
41 00:06:34.790 ⇒ 00:06:44.239 Kaela Gallagher: Yeah, in terms of, like, active clients, our client, engagements are, on average, like, 3 to 6 months.
42 00:06:44.260 ⇒ 00:07:04.819 Kaela Gallagher: So, we always have, like, new projects to work on, but I would say at a time, we’re probably working with, like, 10 different clients. Okay. And there’s, like, a few people working with… with each client. We have, like, an account manager, we have an engineer, we probably have an analyst as well, so…
43 00:07:04.820 ⇒ 00:07:11.390 Kaela Gallagher: All of our engineers are supporting, I would say, like, 2 to 3 clients at a time.
44 00:07:11.390 ⇒ 00:07:14.969 Sam Kartiganer: Sweet, yeah. And then, I think…
45 00:07:15.160 ⇒ 00:07:26.120 Sam Kartiganer: Another question I had was in terms of, like, compensation or salary, is this, like, an hourly-based position? Is it more like…
46 00:07:26.310 ⇒ 00:07:30.520 Sam Kartiganer: Are you wanting full-time? Like, those sorts of… Information.
47 00:07:30.800 ⇒ 00:07:49.529 Kaela Gallagher: Yeah, right now, our entire organization is on a 1099 independent contracting basis, which does mean hourly rates. We’re building out our W-2 capabilities. I just selected benefits for us yesterday, actually, which is really exciting. But I think…
48 00:07:49.590 ⇒ 00:07:59.579 Kaela Gallagher: Still moving forward, we’ll have people be 1099 to start, and then we will, like, do W-2 conversions in waves across the company.
49 00:08:00.230 ⇒ 00:08:24.110 Sam Kartiganer: And I know it’s a consulting, so, like, obviously you fit the needs of the company, but, in terms of this idea of incorporating AI into their products one way or another, like, is there any specific things that y’all do? Obviously, you said you’re pivoting away from more of the just data analytics side and kind of pivoting more towards AI.
50 00:08:24.920 ⇒ 00:08:38.580 Kaela Gallagher: Yeah, so for some context, I think previously the way that we were working with clients was oftentimes pitching the data work, like a lot of Snowflake and DBT, and…
51 00:08:38.970 ⇒ 00:08:49.790 Kaela Gallagher: once we would complete data projects for clients, then it’s a very natural next step to move into different, like, AI features, or chatbots, or things like that.
52 00:08:49.850 ⇒ 00:08:59.060 Kaela Gallagher: So that’s how, like, a lot of our AI business was coming through previously. But now we are at a point where we’re selling to clients
53 00:08:59.150 ⇒ 00:09:02.289 Kaela Gallagher: the AI business to start.
54 00:09:02.480 ⇒ 00:09:14.230 Kaela Gallagher: So, that’s kind of been… been a shift for us, and I think that’s why, you know, even if we’re hiring a data engineer or an analytics engineer, we’re looking for somebody that’s really, like, AI-enabled as well.
55 00:09:14.230 ⇒ 00:09:14.850 Sam Kartiganer: Yeah.
56 00:09:15.070 ⇒ 00:09:20.860 Sam Kartiganer: I… those have been the jobs I’ve been mainly looking for. Data science, I work with
57 00:09:21.050 ⇒ 00:09:40.529 Sam Kartiganer: large language models. I mean, even in my classes, they, which I think is funny now, for any coding class, they make us use AI. Instead of a project that would take, like, you know, two days without AI, they make us… it’s like a… you know, without AI, it would take us 4 weeks, but now they’re expecting us to do it in a week with AI.
58 00:09:40.980 ⇒ 00:09:48.260 Kaela Gallagher: Oh, wow. Yeah. That’s really cool. What kind of, like, models are you guys using?
59 00:09:48.460 ⇒ 00:09:53.390 Sam Kartiganer: I… it’s up to us. I usually use Cloud Code. I think Cloud Code is, like.
60 00:09:53.830 ⇒ 00:09:54.860 Sam Kartiganer: the best.
61 00:09:55.070 ⇒ 00:09:57.670 Sam Kartiganer: Just generally speaking,
62 00:09:57.910 ⇒ 00:10:03.770 Sam Kartiganer: Cause it can look directly into your database, and… or just whatever code you’re using, so…
63 00:10:03.770 ⇒ 00:10:07.410 Kaela Gallagher: Yeah, yeah. We’re using, Cursor.
64 00:10:07.410 ⇒ 00:10:07.940 Sam Kartiganer: Oh, yeah.
65 00:10:07.940 ⇒ 00:10:25.679 Kaela Gallagher: which also has, like, similar features. It’s, like, super integrated into even Slack and Notion for us and stuff, so… Cool. Yeah, I’ve heard… I think that Claude code is pretty similar to Cursor, and you can use some of the Claude models on Cursor, too, so…
66 00:10:25.790 ⇒ 00:10:29.470 Kaela Gallagher: Seems like that would be a natural transition for you.
67 00:10:29.620 ⇒ 00:10:37.079 Kaela Gallagher: I’m curious, on, like, the data side, are Snowflake and dbt familiar to you? Are those.
68 00:10:37.080 ⇒ 00:10:46.229 Sam Kartiganer: dbt, I’ve used a good amount. Snowflake, I’m familiar with. I just finished a data… data course at my class, and they kind of just…
69 00:10:46.450 ⇒ 00:10:55.419 Sam Kartiganer: make us cover everything. Dpt… DBT I’ve used, like, personally, but Snowflake, I have… I have done things on Snowflake.
70 00:10:55.760 ⇒ 00:11:03.840 Kaela Gallagher: Okay, okay, cool. Yeah, we’re using both of those quite a bit with our clients, so that’s why I ask.
71 00:11:04.050 ⇒ 00:11:07.829 Kaela Gallagher: Nice. Any other questions that I could help answer?
72 00:11:08.760 ⇒ 00:11:13.889 Sam Kartiganer: If I were interested, is this…
73 00:11:14.500 ⇒ 00:11:17.540 Sam Kartiganer: something I… you… I did send my,
74 00:11:17.740 ⇒ 00:11:24.730 Sam Kartiganer: a little, like, interview, or not interview, but a, you know, an application over. Would that be enough at the moment?
75 00:11:25.160 ⇒ 00:11:25.520 Kaela Gallagher: Yeah.
76 00:11:25.520 ⇒ 00:11:27.900 Sam Kartiganer: Like, resubmit it with a video or anything like that.
77 00:11:27.990 ⇒ 00:11:43.880 Kaela Gallagher: Yeah, no, this takes place at the video, you’re all good. Yeah, I have your, like, LinkedIn and your resume and stuff, so, everything we need to move forward. I can have you chat with, Awash, who leads, like, a lot of our data efforts.
78 00:11:45.870 ⇒ 00:12:08.210 Kaela Gallagher: And yeah, our interview process would be 3 rounds. Like I said, we might not move through it, like, super quickly, but first round is just, like, higher level experience, second gets a little bit more technical, and then third is, a take-home challenge that you would then, like, bring to the final round and present as if you’re presenting to a client.
79 00:12:08.620 ⇒ 00:12:09.350 Sam Kartiganer: Sweet.
80 00:12:09.590 ⇒ 00:12:10.200 Kaela Gallagher: Cool.
81 00:12:10.570 ⇒ 00:12:17.069 Kaela Gallagher: Awesome! Well, I can send you a follow-up email shortly, but thanks so much for your time today. It was great getting to meet you.
82 00:12:17.070 ⇒ 00:12:17.770 Sam Kartiganer: as well.
83 00:12:17.950 ⇒ 00:12:20.790 Kaela Gallagher: Yeah, of course. Have a good one, enjoy your weekend.
84 00:12:20.790 ⇒ 00:12:21.560 Sam Kartiganer: You too.
85 00:12:21.750 ⇒ 00:12:22.480 Kaela Gallagher: Thanks.