Machine Learning - Engineering Director

Our purpose is to make great financial decision making a breeze for everyone, and that purpose drives us every day. It’s why we’re on a mission to create an automated quoting engine, with the simplest of experiences, wrapped in a brand everyone loves! We’re 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, you’ll 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 leader—someone who can lead people, deliver at pace, and contribute to system design and platform standards. You’ll 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 production

• Drive team planning, estimation, and sprint delivery—ensuring projects are delivered on time and to a high standard
  
• Support the development of real-time and batch ML workflows across a variety of business use cases
  
• Collaborate closely with data scientists to move prototypes into high-quality production systems Platform & Engineering Standards
  
• Contribute to the design and evolution of our internal ML platform and tooling
  
• Champion best practices in CI/CD, observability, reproducibility, and infrastructure-as-code for ML
  
• Ensure all deployed systems meet requirements for resilience, testing, security, and performance
  
• Influence and contribute to shared frameworks, libraries, and deployment pipelines Identify and unblock cross-team dependencies involving data science, platform, and software engineering
  
• Help shape platform direction by feeding back requirements from applied ML delivery Line manage and mentor MLEs, supporting their career development and technical growth
  
• Experience leading engineering teams focused on machine learning, data platforms, or applied AI delivery
  
• Proven 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 environments—balancing scope, prioritisation, and quality
  
• A background in software engineering, MLOps, or data engineering with production ML experience Familiarity with streaming or event-driven ML architectures (e.G. Exposure to large language models (LLMs), vector databases, or RAG pipelines
  
• Experience building or managing internal ML platforms, experimentation frameworks, or feature stores You’ll have the tools and autonomy to drive your own career, supported by a team of amazingly talented people. For us, it’s 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, we’ve pretty much got you covered!#