Principal Machine Learning Engineer

Job Type: Permanent Build a brilliant future with Hiscox Principal Machine Learning Engineer

Location:

London/York

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.

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 ough 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.

• 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, security, and developer experience.
  
  Contribute hands‑on to critical systems, frameworks, and platform components where the complexity or impact demands it. Define best practices and guardrails for the use of AI‑assisted coding tools, ensuring they are used to improve productivity and code quality without compromising maintainability or operational safety. Lead by example in applying AI‑assisted development techniques (e.g. for prototyping, refactoring, and accelerating complex engineering work) with strong engineering judgement.
• ML Platform Strategy & Build‑Out Partner closely with Group and Platform teams to design, build, and evolve the ML Platform, ensuring it supports:
  • Reusable and scalable deployment patterns
  • CI/CD for machine learning
  • Full model lifecycle management
  • Monitoring, observability, and alerting
  • Secure and compliant operation
  • Shape platform standards and interfaces that enable consistent ML delivery across squads and value streams
  Lead technical spikes and proof‑of‑concepts to evaluate new tools, approaches, and architectural patterns. Influence developer tooling choices so that AI‑assisted coding tools integrate safely with CI/CD, testing,…