🚀 Member of Technical Staff - Agentic AI 🚀
Frontier AI / Agent Infrastructure Start-up
đź“Ť London, Full Time Onsite
đź’¸ ÂŁ100k - ÂŁ150k + up to 0.5% equity depending on level
âś… High agency
âś… Steep growth trajectory
âś… Early stage, meaningful equity
This company sits in a part of the AI ecosystem that most people haven’t really seen yet.
They’re not building a single product. They’re building the layer that sits behind how AI systems are evaluated, improved, and ultimately trusted in real-world environments.
That sounds abstract, but in practice it’s very real.
They’re already working closely with some of the most advanced AI teams globally, helping tackle a problem that’s still wide open: how do you reliably evaluate whether an AI system is actually performing well, and how do you generate the data that makes it better over time?
What’s interesting is how they approach it.
At a surface level, they combine domain expertise with structured evaluation — bringing in highly specialised perspectives to assess outputs in real-world contexts. But that’s not really where the long-term value sits.
The deeper layer is what they’re building internally. They define what “good” looks like, design evaluation frameworks, and create feedback loops that directly improve how models behave. That’s the part that actually shapes how systems get better in production.
Alongside that, there’s a genuine research angle. The work feeds into how modern AI systems are being tested, evaluated, and iterated on in practice — not just in theory.
The reason this role stands out is that you’re not sitting in one slice of that world.
You’re seeing how different teams approach building AI systems, how evaluation and data actually drive performance, and how things move from something that looks good in a demo to something that works reliably in production.
This is a genuinely early hire.
You’d be working directly with the founders, helping define how these systems are built and evaluated, while also contributing to product and technical direction. It’s not a role where you come in and pick up tickets — you’re shaping the system itself.
The environment is intense, and they’re very open about that.
This only really makes sense if you view it as a period of acceleration, not a long-term lifestyle.
If you’re early in your career, already building AI systems, and motivated by getting very good, very quickly, this kind of environment can compress years of learning into a much shorter window.
You’re close to real problems, working directly with experienced builders, and seeing how things actually get deployed — not just discussed.