We find and personally screen the rare profile: remote-native, no-ego, high-agency, builder mentality. The kind of engineer who thrives in a lean AI-native team and doubles output where others slow down. Built on years of hands-on technical due diligence and a network we have curated ourselves.
There is a meaningful difference between an engineer who works remotely because they have to and one who has built their entire career that way. We look for the latter: autonomous, async by default, over-communicates in writing, and does not need management to stay in motion.
The best engineers in lean teams are not the ones with the most impressive CVs. They are the ones who care more about the outcome than the credit, who flag problems early, and who treat the product like it is theirs. We screen for this specifically - it is not something a CV shows.
The engineers who compound in value over the next few years are the ones who embrace AI tools as force multipliers rather than resenting them as threats. We find people who have already made that shift - who have figured out how to use AI agents inside their flow, not around it.
We screen the way we assess engineering teams in a Tech Due Diligence: on both dimensions at once. Technical capability and personal fit are not separate conversations. They happen together, by people who have done this hundreds of times.
We evaluate real work, not test exercises. Code quality, architectural thinking, how they approach problems they have not seen before. We look at GitHub history, previous projects, and how they talk about the decisions they made. After 80+ tech due diligences, we know the difference between someone who writes code and someone who engineers software.
A structured conversation about how they actually work: how they communicate async, how they handle ambiguity, how they respond when a project changes direction, what they are like when something goes wrong. We have learned over years that technical skill is necessary but never sufficient. Character is what makes a remote hire succeed or fail.
The candidates we are serious about complete a short case study in two parts. The first assesses how they think: how they approach an unfamiliar system, what questions they ask, how they structure their reasoning under uncertainty. The second assesses how they build: specifically, how they work with AI tools, because that tells us something different and equally important. We have found that both dimensions matter, and that you cannot infer one from the other.
We speak to people who have worked with them, not just the references they provide. We know what questions to ask to get past the polished answer and understand what it is genuinely like to work with this person.
You receive a shortlist of two to four candidates, each with a written brief covering our technical assessment, our personal read, and our recommendation. Not a stack of CVs to filter through. Our view, clearly stated, so you can make a fast and confident decision.
You are building lean and moving fast. You cannot afford a bad hire, and you do not have six months to run a hiring process. You need someone who will be autonomous from day one and make the team better, not just bigger.
You have a live product and early traction. You know what your first engineering hire needs to look like: someone who can work alongside AI agents, own the codebase, and scale the system as the product grows. We know exactly what that profile requires.
You went through a recruiter, or LinkedIn, or a referral, and it did not work. Not because the person was not skilled, but because the fit was wrong in ways that only became clear six months in. You want someone who screens for the things that actually matter.
The profiles we surface are people we know, have worked with, or have been referred to us by people we trust. We do not spray job postings and filter applications. We reach out directly to the right people - many of whom are not actively looking but would move for the right opportunity.
We have spent years doing Tech Due Diligence - assessing engineering teams, reviewing codebases, interviewing CTOs. When we evaluate a candidate technically, we are doing it with the same rigour we would apply to a 80+ point DD assessment. A recruiter cannot do that. We can.
We work with many of our clients across multiple engagements - as their Fractional CTO, on their AI strategy, building their product in the AI Venture Studio. When we place someone, we often continue working alongside them. That changes how carefully we screen.