Our purpose is to make great financial decision making a breeze for everyone, and that purpose drives us every day.Its why were on a mission to create an automated quoting engine, with the simplest of experiences, wrapped in a brand everyone loves!As a Staff Machine Learning Engineer, youll play a pivotal role in designing, scaling, and evolving the machine learning infrastructure that powers Compare the Markets most ambitious AI products. From LLM-based personalisation to real-time optimisation systems, youll help define how models are developed, deployed, and maintained in productionreliably and responsibly. Youll work across product, data science, and engineering to lead delivery of complex ML systems. Youll also define the core MLOps capabilities for the business and establish the standards and patterns that accelerate safe, scalable AI deployment across teams.ML Systems Design and DeliveryLead the architecture and delivery of ML systems that power real-time and batch predictions at scaleDesign production pipelines for training, deployment, and monitoring using modern MLOps toolingTake ownership of technical quality, resilience, and observability of critical ML servicesBuild reusable tools and frameworks to enable fast, safe experimentation and deploymentPlatform, Standards and MLOps FoundationsDefine and build the core MLOps capabilities for the organisation, including training pipelines, deployment frameworks, and observability toolingEstablish standardised patterns and best practices to accelerate model development, testing, and deploymentLead the evolution of our ML platform, working with engineering partners to improve scalability, governance, and developer experienceContribute to responsible ML practicessupporting auditability, explainability, and model health monitoringPartner with data scientists to take models from prototype to production with clear interfaces and robust engineeringProvide mentorship, pair programming, and code reviews for other engineers across the AI functionStay ahead of developments in MLOps, LLM infrastructure, and AI engineering best practicesInfluence long-term strategic direction for ML tooling and delivery across the organisationHelp build a high-performing, inclusive, and collaborative ML Engineering cultureExtensive experience designing and deploying ML systems in productionDeep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI)Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Strong understanding of ML system lifecycle: testing, monitoring, governance, observabilityA background in software engineering, computer science, or a quantitative fieldor equivalent experience leading ML systems in productionYoull have the tools and autonomy to drive your own career, supported by a team of amazingly talented people.For us, its not just about a competitive salary and hybrid working, we care about what matters to you. From a generous holiday allowance and private healthcare to an electric car scheme and paid development, wellbeing and CSR days, weve pretty much got you covered!#