Job Description:

Senior Machine Learning Engineer | Cambridge / Hybrid | £80,000–£120,000 + Bonus

Join a fast-growing FinTech/InsurTech company in Cambridge that is transforming how financial and insurance products are built using machine learning and data-driven decision-making.

Their platform leverages advanced ML models to power areas such as fraud detection, risk modelling, underwriting optimisation, and customer analytics, enabling smarter and faster decisions at scale.

With strong investment and a product-led engineering culture, they are looking for a Senior Machine Learning Engineer to play a key role in building and deploying production-grade ML systems.

The Role

• Design, build, and deploy machine learning models for fraud detection, risk scoring, and predictive analytics
• Develop scalable ML pipelines and work closely with data engineering teams
• Collaborate with product and domain experts to translate business problems into ML solutions
• Optimise model performance and ensure reliability in production environments
• Contribute to architecture and best practices across ML and MLOps

Key Skills & Experience

• 4+ years’ experience in machine learning or AI roles
• Strong Python skills, with experience in frameworks such as PyTorch, TensorFlow, or Scikit-learn
• Experience deploying ML models into production, including MLOps, CI/CD, Docker, and Kubernetes
• Solid understanding of statistics, data modelling, and software engineering principles
• Experience with cloud platforms such as AWS, GCP, or Azure
• Exposure to financial services or insurance domains is advantageous, but not essential

What’s in It for You?

• Work on high-impact ML systems solving real-world financial and risk challenges
• Join a collaborative, engineering-first environment with strong technical ownership
• Competitive salary of £80,000–£120,000, plus bonus
• Flexible hybrid working with a Cambridge-based office
• Clear progression and the opportunity to influence ML strategy

What’s in It for You?

Apply now or reach out directly for a confidential discussion about this and similar opportunities.