Transcript
there's a lot of support that is gonna be needed for this. So if I am hiring an engineer, I don't do it that much. Like I'm doing it maybe once a year, once a quarter. But either way, you guys are doing it every single day. And so if I'm doing this, especially now that it's so new, I don't know what I don't know. And I definitely don't know a lot. And so you need a partner. Which is our second thing: we fervently believe that the future of work is not directly connecting with individuals. It's directly connecting with new types of partners that are directly connected with individuals.
So you validated the two things, which is there's a place for talent leaders, and the second thing is you need to find the right partner versus doing everything yourself. So let's literally dig into the science of what makes Remotely so powerful. And we can start with the massive research project you did across the GitHub repos if you want. I'm just gonna give you the floor. Show off, brag and tell us data on what's made y'all so good.
Sebastian Alvarez: So I can tell you a little bit about the technology that we built at the beginning, how we bootstrapped our database of engineers, our network as we called it, and then how we cheated a little bit. So when we started Remotely, I only knew the people who worked with me and my friends from Cordoba. So I was like, okay, I can do this, but I can only tap into these people in Cordoba. They're all great people, they're very good engineers, but I will probably need more than people that I know about.
So we heard about these people from Spain who were doing GitHub analysis and they basically were looking at every commit that happens on GitHub and analyzing those commits. So Cabo called them and we said, hey, we want to help you build this but we want to build it in a way that we can map every engineer in the world, learn about what they do, what they are doing, what they are good at. And use that to help them get work in the US. So basically, can we tap into every engineer in the world outside of the US and help them find a job in the US? That was our plan.
So what we did is that we literally analyzed the firehose from GitHub, analyzing every commit that happened. We were mapping them to people. We were learning the technology they use, how many, if they did more commenting than writing code, how many pull requests they created, how many other people they worked with. So we were mapping GitHub public commits. All of this was information that people were putting out there. We were just mapping it. And we used that to bootstrap our database of engineers.
So we started to connect with these people. We were on one side like every marketplace, right? We were on one side looking for open positions, we were getting customers opening their positions and then using those job openings to tap into this GitHub database that we built and say, okay, these five thousand GitHub handles seem to be good profiles for this position. And that's when we started doing the profiling.
So our first filter was English. So we started having conversations with all of them. We would record them and ask them questions about what they like to do, their best technical efforts that they did, where they were working. And one of the questions that we asked a lot was what type of work do you want to find? Because we used that to match cultural fit, which is what I said before.
For that, we found a team of sisters from Cordoba, one is a psychologist and the other one was an English teacher, who were doing HR I think their whole life. And they were incredible, they were an incredible team doing this cultural fit matching that I'm speaking about. So they had a very good sense for understanding what was important for the customer, in the sense of cultural fit. Like what do they care about? The type of question they would ask was, what type of person do you want to work with in your team? They would ask questions about how the team was built, where they were finding these people, what they value the most. And using those signals when we interviewed candidates, and we had a huge network of candidates, we were trying to match the signal.
And these type of candidates would say, I prefer to work at a startup, or I prefer to work in a bigger company. Or some people say, I worked at a startup and it burned me out. Or I worked for two companies that went to hell and I don't want that to happen to me again. So we say, okay, this is not a great candidate for a startup. This is a great candidate for a scale-up or for a bigger company. And we would tag these candidates in such a way that once we got the customer, we knew where to find them.
So because we were looking at the GitHub, we already had a lot of signal on the technical side. We already knew their technologies, we already knew how good they were at writing code, at writing comments, how much people would accept in their pull requests and all that. And we were matching that with personality, cultural fit type of information, and we started matching and it worked.
And a lot of times, like you said, reps is what gets you there. And we just did that a bunch of times. I don't know the numbers now, but we have almost eight thousand accepted candidates in our network from more than two hundred thousand people that we probably tapped into, talked to, interviewed and all that. And on the same side on the customer, so we know how to profile customers and we know how to profile people and try to match them.
