Company: Mondrian Alpha
Location: London
Posted: May 17th, 2026
Your Code Moves Markets | Quantitative Developer | Systematic Quant Fund | London
The engineers who thrive here are not the ones who wait to be told what to build.
Our client sits closer to a world-class technology company than a traditional financial firm. They are a data and technology-driven trading business that has built its edge entirely through engineering and research, with no reliance on discretionary judgment or market intuition. The infrastructure they operate is the product. Getting it right is everything.
We have worked closely with this firm for a number of years and have placed multiple engineers across their teams. The people already here are exceptionally good and unusually enjoyable to work with. Both things matter in equal measure.
The Environment
This is not a support function and it is not a role with a fixed brief. You will work directly inside the research and production machine, on problems where the line between engineering and research is genuinely blurred. The firm does not require finance experience. Strong engineers from top-tier technology companies have joined here and thrived, and in many cases brought something the firm could not have hired from within the industry. What they cannot teach is engineering instinct. What they will not compromise on is the quality of the people in the room.
The Work
You will operate across a research and production environment spanning the full stack from signal development through to live deployment. Depending on your background and where you add most value, the work could take you across any of the following:
Modelling how trading activity moves markets. When size moves through a market, how does price respond, and how do you build systems that understand and predict that dynamically in real time? This is ML research and live systems engineering working as a single discipline. The team is in a deliberate build phase with a strong bank of research ideas already in place, and the engineering capability being built around them now.
Building the compute infrastructure that enables researchers to develop, iterate, and run signals at scale. The goal is a research function that is faster, more capable, and able to push into increasingly complex territory. This is not tooling that sits to the side of the real work. It is infrastructure that sits underneath it.
Solving how increasingly complex models get deployed and served efficiently in live environments. The specific challenge right now is moving from CPU-only signal hosting to GPU-accelerated inference across a large and growing signal library. The production environment uses a typed, compiled language. You do not need to arrive knowing it, but you need to be comfortable in that paradigm and serious about learning.
The bar here is not knowing the framework. It is knowing how to use a language to get the best out of the hardware underneath it.
Python is the primary language across the teams. The technical bar is high throughout: clean structured code under pressure, strong algorithmic thinking, and the ability to reason about system design from high level architecture down to implementation detail.
Get in Touch
If this resonates, reach out directly. We have deep visibility across this firm's teams and can give you a genuine picture of the problems, the people, and whether this is the right move for you right now.