Principal Machine Learning Engineer

## Job Type Permanent ## Build a brilliant future with Hiscox Principal Machine Learning Engineer ## Location London/York ## Why Hiscox London Market Hiscox London Market sits at the centre of global specialist insurance, tackling some of the most complex and unusual risks in the world. These are not commoditised problems, they demand deep expertise, strong judgement, and increasingly, sophisticated data and machine learning capabilities. We have a strong track record of putting AI into real production use, from augmenting underwriting decisions to shaping future market standards through partnerships and market‑first innovation. This is an environment where advanced ML systems are expected to operate reliably, safely, and at scale, not remain in experimentation. You’ll join a culture that values technical excellence, ownership, and courage, where senior individual contributors are trusted to set direction, challenge thinking, and build platforms that matter. For a Principal Machine Learning Engineer, this is a chance to work on high‑impact ML systems, influence how AI is adopted across the London Market, and help shape the future of insurance. ## Role Purpose As a Principal Machine Learning Engineer (MLE), you bring a wealth of experience in building, scaling, and operating production machine learning systems, and use that experience to provide deep technical leadership across machine learning engineering and MLOps. You play a key role in shaping the architectural strategy for production ML systems and the ML Platform, working closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. Through hands‑on contribution, design leadership, and technical mentorship, you help teams navigate complex technical decisions and build robust, maintainable systems. A central focus of the role is enabling the organisation to move quickly without sacrificing quality, evolving the ML platform, supporting the transition from experimentation to production, and helping teams adopt modern engineering practices. This includes championing the effective and responsible use of AI‑assisted development tools as part of a broader approach to improving developer experience, system quality, and long‑term sustainability. Success in this role comes from the practical application of deep experience, strong architectural thinking, and the ability to help others build better systems that deliver real business value. ## Key Responsibilities ### Technical Leadership & Ownership (Individual Contributor) - Act as the technical lead for Machine Learning Engineering and MLOps across London Market. - Technically lead the most complex and business‑critical ML systems, from architectural design through to production operation. - Define and evolve production ML patterns and best practices, covering deployment, orchestration, monitoring, retraining, and decommissioning. - Lead deep technical decision‑making, balancing scalability, reliability, and maintainability. - Champion the effective and responsible use of AI‑assisted development tools to improve developer experience, system quality, and long‑term sustainability. ### Platform & Architecture - Shape the architectural strategy for production ML systems and the ML Platform. - Work closely with Data Science, Engineering, and Platform teams to define patterns, standards, and tooling that enable reliable, repeatable delivery at scale. - Drive the evolution of the ML platform, supporting the transition from experimentation to production. - Ensure systems are designed for scalability, reliability, and maintainability. ### Team & Culture - Provide technical mentorship and guidance to teams, helping them navigate complex technical decisions. - Foster a culture of technical excellence, ownership, and courage. - Encourage and support the adoption of modern engineering practices across the organisation. ### Innovation & Impact - Influence how AI is adopted across the London Market. - Help shape the future of insurance through the development of high‑impact ML systems. - Ensure that ML systems deliver real business value and contribute to the organisation’s strategic goals.