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!Were scaling our AI capabilities at Compare the Market, and Machine Learning Engineering is at the core of how we turn models into production-ready systems. As a Machine Learning Engineering Manager, youll lead a team of MLEs responsible for building, deploying, and maintaining the ML infrastructure that powers our personalisation, optimisation, and intelligent decision-making products.This is a hybrid role for a hands-on engineering leadersomeone who can lead people, deliver at pace, and contribute to system design and platform standards. Youll partner with data science, analytics, and platform engineering teams to accelerate how AI is developed and deployed across the organisation.Lead a team of MLEs delivering robust, scalable machine learning systems into productionDrive team planning, estimation, and sprint deliveryensuring projects are delivered on time and to a high standardSupport the development of real-time and batch ML workflows across a variety of business use casesCollaborate closely with data scientists to move prototypes into high-quality production systemsPlatform and Engineering StandardsContribute to the design and evolution of our internal ML platform and toolingChampion best practices in CI/CD, observability, reproducibility, and infrastructure-as-code for MLEnsure all deployed systems meet requirements for resilience, testing, security, and performanceInfluence and contribute to shared frameworks, libraries, and deployment pipelinesIdentify and unblock cross-team dependencies involving data science, platform, and software engineeringHelp shape platform direction by feeding back requirements from applied ML deliveryLine manage and mentor MLEs, supporting their career development and technical growthExperience leading engineering teams focused on machine learning, data platforms, or applied AI deliveryProven track record deploying ML systems in production at scale (batch and/or real-time)Strong technical background in Python and ML engineering tooling (e.g. MLflow, Airflow, SageMaker, Vertex AI, Databricks)Understanding of infrastructure-as-code and CI/CD for ML systems (e.g. Ability to lead delivery in agile environmentsbalancing scope, prioritisation, and qualityA background in software engineering, MLOps, or data engineering with production ML experienceFamiliarity with streaming or event-driven ML architectures (e.g. Exposure to large language models (LLMs), vector databases, or RAG pipelinesExperience building or managing internal ML platforms, experimentation frameworks, or feature storesYoull 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!#