Algorithm Engineer

At OQC, we aren’t just theorising about the future; we’re building it. Born from a philosophy of bold innovation, we’ve transitioned quantum computing from an academic ambition into a commercial reality—and we’re just getting started.

The Purpose

Apply your financial-services expertise to build practical algorithms and applications that bring classical and quantum computing together.

As our Algorithm Engineer - Financial Services, you’ll select, adapt and implement methods for banking, fintech and payments applications. Working alongside quantum algorithm engineers, you’ll help connect established approaches with emerging quantum capabilities and assess their practical value against strong classical alternatives.

Your focus will be on turning technical capability into useful applications, combining hands-on implementation with an understanding of what financial-services organisations need from their technology.

The Role

Working within our Product team and reporting to Product and Engineering leadership, you’ll be a senior, hands-on individual contributor collaborating across algorithm engineering, software and technical product development.

You’ll bring financial-domain knowledge to the team, helping select suitable methods, implement algorithmic components and integrate them into wider applications. Alongside this development work, you’ll contribute rigorous evaluation: understanding how methods perform, where their limitations lie and whether the results are meaningful in an operational setting.

This opportunity could suit someone already building algorithms into commercial applications, or a researcher whose work is closely connected to business needs and practical application. Previous quantum-computing experience is not essential.

The role is based in King’s Cross, London, with hybrid working and regular time together in the office.

What You’ll Be Working On

• Select, adapt and implement classical algorithms and machine-learning methods for financial-services applications, including areas such as fraud detection, credit risk and transaction modelling.
• Collaborate with quantum algorithm engineers and technical product colleagues to combine domain expertise with quantum and hybrid methods, helping integrate algorithmic components into useful applications.
• Build strong, production-relevant classical detection and prediction baselines, enabling meaningful comparisons with quantum and hybrid approaches.
• Define classical synthetic-data baselines against which quantum-generated data can be assessed.
• Evaluate practical usefulness, privacy, calibration and robustness over time, considering operational requirements and customer acceptance.