Quant Software Engineer — Build the Systems Behind Real-World Trading Intelligence : £300k+

We’re looking for a

Quant Software Engineer

who thrives at the intersection of rigorous mathematics, high-performance engineering, and real-world financial systems. This is a role for someone who enjoys turning complex quantitative ideas into robust, production-grade software that runs at scale—and under pressure.

The Role

You will work closely with quants, researchers, and traders to design and implement systems that power pricing, risk, and trading strategies. Your work will directly influence live decisions in fast-moving markets, where correctness, latency, and reliability all matter at once.

This isn’t just a “build models” role, and it isn’t just “write backend services” either. It’s both—plus the engineering discipline to make it all hold together in production.

What You’ll Do

• Design and build high-performance systems supporting quantitative research and trading workflows
• Translate mathematical and statistical models into production-grade code
• Develop and optimise low-latency services for data processing and execution
• Build robust data pipelines, simulation frameworks, and analytics tooling
• Collaborate closely with quantitative researchers to refine models and assumptions
• Improve research-to-production infrastructure and deployment workflows

Tech Stack

We don’t expect you to know everything—but experience in parts of the following stack is highly relevant:

• Core Languages:

C\+\+, Python, Rust

• Quant \& Data:

NumPy, pandas, SciPy, Jupyter

• Data Infrastructure:

Kafka, Redis, PostgreSQL, kdb\+ (or similar time-series databases)

• Distributed Systems:

Microservices architectures, gRPC, REST APIs

• Cloud \& Infrastructure:

AWS / GCP, Docker, Kubernetes

• Performance \& Systems:

Low-latency optimisation, multithreading, memory management

• CI/CD \& DevOps:

Git, CI pipelines, automated testing frameworks

What We’re Looking For

• We’re looking for engineers who combine strong software fundamentals with genuine quantitative curiosity.
• You should be comfortable with systems-level thinking, performance optimisation, and writing clean, maintainable code in a production environment. A strong background in one or more of C\+\+, Python, or similar languages is expected.
• You’ll likely have experience with data structures, algorithms, and distributed systems, and you’ll be confident reasoning about trade-offs between speed, accuracy, and complexity.
• A degree in Computer Science, Mathematics, Physics, Engineering, or a related discipline is typical—but what matters most is how you think and build.
• Exposure to quantitative finance, trading systems, or numerical methods is a strong plus, but not strictly required if you can demonstrate equivalent depth elsewhere.