Backend Software Engineer, Quantexa

• Strong commercial experience in Java or another JVM-based language
• A willingness — and enthusiasm — to learn Scala and adopt functional programming practices
• Pride in designing, building, and delivering well-engineered, high-quality solutions to complex problems
• A big-picture approach to problem-solving, ensuring your solutions work effectively within the wider system
• Experience across the full engineering lifecycle, from design and implementation through to testing and deployment
• A passion for growth and learning as part of one of the UK’s fastest-growing scale-ups

Desirable

• As a Backend Software Engineer at Quantexa, you’ll play a key role in shaping one of the most critical components of our Decision Intelligence platform: Decision Systems
• The Decision Systems team is responsible for turning complex, interconnected data into meaningful insights — helping global organisations detect patterns, uncover risks, and make smarter decisions. You’ll work across the software stack, from improving our scoring capabilities that help uncover financial crime, to building tools that make detection logic faster, clearer, and more powerful for our users. The work you do will directly impact how clients identify suspicious activity and protect their businesses
• This role isn’t just about writing code — stakeholder collaboration will become an increasingly important part of your remit. You’ll have the autonomy to take features from concept all the way through to release, working closely with product teams and delivery specialists to design solutions that have real-world impact. You’ll join a talented, supportive group that values innovation, knowledge sharing, and continuous improvement
• The engineering work in the Decision Systems team centres around backend development using Scala and the latest agentic AI tooling. Typical tasks include:
• Building new tooling that allows users to configure detection logic more easily and effectively
• Enhancing the platform so that existing Scores can be applied across an expanding range of scenarios
• Improving system performance to ensure even the largest datasets can be analysed efficiently