A bootstrapped founder's hiring playbook

Pavlo, a bootstrapped founder generating over $500K/month—discusses hiring: how he prioritizes problem-solving over credentials, his thoughts on ageism, and how he hires entry-level talent.

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Bootstrapping a startup is like playing an already challenging game in hard mode. I had a great conversation with Pavlo Pedenko, Co-founder of Mathema – a mathematics tutoring marketplace – about his path to entrepreneurship, his startup's mission and his unconventional approach to hiring, one that results in him giving people who might not look like a great fit on paper, a chance.

We were not hiring for an email marketing candidate but came across someone in their fifties with relevant experience. We offered to work with him part-time and hired him full-time after a brief stint. Many startups are likely to have overlooked him based on his age. If there's one problem with the job market in general that bothers me, it's ageism. It's hard to find a job in your 50s.

Pavlo Pedenko, Co-Founder of Mathema

This unconventional approach appears time and time again during our conversation. So far, Pavlo has not raised funding from VCs, has hired from unconventional backgrounds and has used AI in truly novel – yet thoughtful – ways. We start with his founding story:

Mathema's Founding Story

Pavlo is a former PM at Wise (Low-cost global money transfers) and Preply (language learning platform). The founding story behind Mathema.me is unique in that Pavlo was contacted by his co-founder once he had an initial MVP working.

My co-founder contacted me, saying, Hey, I know you worked at Preply. I'm testing this idea of selling math tutors to parents. He asked me if I wanted to work with him on this idea and see if we could build something out of it.

The obvious answer when people think of a "tutoring marketplace" is language learning. That's because of geographical arbitrage, i.e. taking a tutor from a less wealthy country and connecting them with a student from a more prosperous country. In Mathema's case, though, this isn't the formula:

Maths tutoring is more nuanced. You need instructors who know local curriculums and speak the language. Despite the added complexity, I loved this idea because I knew the market was huge. At the time, mathematics represented 50% of the entire K-12 tutoring market because, in almost every developed country, mathematics was mandatory.

Aside from the market size, starting a company in this space is somewhat personal for Pavlo.

I'm from Ukraine, a country that gained independence about 30 years ago. Our education system is not highly developed and largely relies on the framework we inherited from the Soviet Union. It focuses heavily on STEM, not so much on humanities. To get into a good school, you must be good at Maths. My family wasn't wealthy: my mother was a librarian, and my father was a policeman. Being good at Maths was my way of getting onto a good path.

I asked Pavlo about Mathema's mission, and he told me, "We don't really have one; it's not necessarily a story VCs love to hear because they all want to hear that we help people become AI professionals. We want more kids to learn mathematics because, down the line, that will lead them to a better life". Ironically, that last sentence is the most inspiring startup mission I've heard in a while, but I see how it might differ from what a VC would want to hear.

So far, avoiding the VC route has worked out well for Mathema. Pavlo's been able to focus on building a product without spending much time playing the fundraising game. But it's not all upside. I've had the luxury of working at companies that can throw money at the problem of hiring great talent. I'm especially curious to learn how he does this without a hefty bank balance to support it.

The bootstrapped hiring playbook

We look for people genuinely interested in figuring out hard—but sometimes boring—problems. You should always want to tap into these types of problems. They're not the sexiest. But this is what startups are about. We don't hire people who want a nice and neat Jira ticket to work on. Because of this, we are flexible when hiring: hard skills don't need to be a perfect match.

As shiny as some startups might look from the outside, most of the ones I've encountered are anything but on the inside. Hiring people who care deeply about the problem and have a "whatever it takes" attitude matters WAY more than how good a candidate might seem on paper.

We set up new hires for success by supporting them during onboarding. If they're smart enough, they'll figure out the hard skills. What's more important is whether they're genuinely interested in the problem we're trying to solve and willing to put the extra effort into making it work. You can't change people's motivations, so finding a committed hire who wants to work hard from the get-go is easier.

We then discuss the balance founders need to strike between being open about how hard startups are versus selling the bigger picture. To find those "committed" hires who care deeply about the problem, founders must sell the mission well. But that should never come at the expense of being open about the challenges:

Selling the company is complicated because you don't want to oversell. But you also don't want to scare candidates away: you want to appeal to the impact that they could have on the mission. I'm being open with candidates about the challenges we face while also being optimistic about these challenges. If there's one thing that you, as a founder, should learn, it's to be relentlessly optimistic.

Most successful founders I've worked with share this trait. Some may see it as delusional, but I've concluded that a certain level of delusion might be necessary to disrupt an industry or create a breakout startup.

We also discuss his approach to hiring, which may seem counterintuitive at first.

One of your roles as a founder is always meeting great talent, even if you're not hiring for a relevant role. In our case, this is how we hired an email marketer. We had yet to crack this channel but knew it was a good way to reactivate customers. We stumbled into a candidate who was a pro. Rather than spend too much time on interviews, I offered him a part-time job to see if he could work well with the team. He's been one of our best hires to date.

A missing part of this story is that this candidate was in their 50s. We discussed how most startups would probably not give an older candidate a second look – ageism exists in startups – but this wasn't an issue for Pavlo. He's focused on how good candidates are at the job vs how good they might seem on paper. Handy for a founder who might not have the resources of his VC-backed peers 💡

AI in recruitin

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When we contacted recruiters, we concluded that we couldn't afford them. For the fees those recruiters quoted, we were better off doing it ourselves. Whilst I have yet to use AI for hiring, I see an opportunity to use it to help with a lot of the screening. I'm keen to experiment with Synthesia, an AI avatar service, which I've found helpful for other tasks at Mathema.

I'll admit, when I initially heard this, I was sceptical. Would a candidate want to engage with an AI avatar during an interview? But Pavlo sees it as a way to support face-to-face interviews rather than replace them. He also sees it as an opportunity to communicate with the candidate asynchronously, saving time for both the candidate and himself.

At this stage, I need to assess whether their salary expectations match what we could create, whether they're happy with how we work and whether they align with our aspirations. Candidates appreciate it at this stage because they can answer these questions on their schedule. If both sides are happy at the end of this, we schedule a technical interview with my engineers. If they pass the technical round, I hop on an actual call with them and do a vibe check.

We also discuss how he runs interviews and ensures candidates do not use AI to answer technical questions during video interviews.

Our engineers were quite concerned with candidates using AI when doing a technical round. So they ask questions on the spot, not using questions that are publicly available on leetcode or other platforms. We also ask the candidate to share their screen when solving problems. We did this to bypass the risk of candidates using chat-GPT to get responses.

I challenge him on this point, as his internal engineering teams do use copilots to code.

I understand your question. If I don't care about this on the job, why would I care about it while hiring for the job? I understand your challenge, and you might be right. But it's how we've ensured we can test their actual skills.

There is a catch-22 here. If they allow candidates to use copilots too much, how much are they evaluating a candidate during an interview? But it's not testing how the candidate will work on the job if they don't allow them to use a copilot. Aside from seeing how the candidate performs on the job with a paid work trial, I don't see a good answer here. But these are not feasible for all startups, especially not bootstrapped ones conserving runway.

We wrap up this point by discussing where he thinks AI can help with hiring and where it will be limited.

I am still looking for a solution that tells you whether a candidate would be a good fit for a job. Ultimately, it's your judgment as a hiring manager that matters. I hope AI will be able to help more in evaluating soft skills, things like drive and resourcefulness. I've found traits for candidates who do well, like people who have experienced other cultures and countries. These are the insights I'd find most valuable from AI.

Closing thoughts

Pavlo's contrarian nature is a common thread in our conversation. He's not chasing the latest shiny thing (either in fundraising or in his approach to hiring), which has resulted in him working on an inspiring mission to train the next generation of mathematicians and give them a path to a better life. Funnily enough, Pavlo and I have both experienced those benefits in our own lives (I'm a Maths and Comp sci grad myself 🤓).

I expect to see more startups that don't necessarily fit the VC mould being started and scaling successfully because of AI and their ability to do more with less.

Pavlo's story reminds us that some of the most impactful companies aren't making headlines for massive funding rounds—they're quietly building products with inspiring missions. In Mathema's case, one that enables genuine social mobility with a team that cares deeply about that mission. In a world obsessed with credentials and conventional paths to success, we need more founders like Pavlo, who are breaking the mould and willing to bet on the underdogs.