Software Engineer: AI & Finance

Role: Quant Developer
Client: Ambitious, early‑stage quantitative investment firm
Compensation: Excellent base plus bonus + Equity on offer
Location: London

Overview We’re partnering with an ambitious, early‑stage quantitative investment firm that’s quietly assembling a world‑class engineering and research team. Backed by experienced founders and operating at the intersection of markets, data, and machine intelligence, this fund is building high‑performance trading systems across equities, crypto, and futures. They’re now looking to add a Quant Developer who thrives in a fast‑moving start‑up environment and wants real ownership over mission‑critical trading infrastructure.

This is not a siloed role. You’ll sit close to the research, trading, and execution stack, helping to design, build, and scale systems that directly impact P&L from day one. You will contribute broadly as a hands‑on engineer—stepping in across software and quant‑development workloads as needed in a demanding startup setting.

• Build, maintain, and evolve production‑grade trading systems that manage and execute orders across multiple MFT strategies and exchanges in equities, crypto, and futures.
• Optimize performance, reliability, and latency across the execution stack, with a strong focus on robustness in live trading environments.
• Partner closely with quant and machine learning researchers to continuously refine data pipelines, signals, and execution logic.
• Contribute broadly as a hands‑on engineer—stepping in across software and quant‑development workloads as needed in a demanding startup setting.

• Strong experience building high‑performance, production‑quality code in a trading or similarly latency‑sensitive environment.
• Strong BSc, MSc or PhD academic background in Machine Learning, Computer Science or Engineering.
• Advanced Python skills; experience with PyTorch, Tensor Flow, Num Py, pandas, scikit‑learn.
• A pragmatic, problem‑solving mindset—someone who enjoys shipping, iterating, and improving real systems.
• The flexibility and ownership mentality required to succeed at an early‑stage fund.

• C++ or other low‑level language experience
• Distributed computing/large‑scale data pipelines
• Prior systematic/prop trading or quant fund experience
• Equities mid‑frequency

• Start‑up culture with genuine roadmap to $1billion AUM.
• Equity on offer alongside excellent base plus bonus.
• Machine learning is core to the investment process, not an overlay.
• Exposure to multiple asset classes within a unified research framework.
• Opportunity to influence model design, data architecture and research standards.