Machine Learning Engineer
Function: Data & AI Solutions
Location: Hybrid, London office
Curious about what’s next?
So are we. Join Compare the Market and help to make financial decision making a breeze for millions.
At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you’ll fit right in.
We’d love you to be part of our journey.
Compare the Market is building AI systems that connect directly to how millions of people in the UK find and buy financial products - and as a Machine Learning Engineer, you will play an important role in making those systems production-ready. That means contributing to the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make the whole thing work.
This is a hands-on role where you will grow your technical skills across a fast-moving and expanding set of AI capabilities and systems. You will work closely with data scientists, engineers, and product teams - delivering well-scoped ML and AI features, contributing to engineering standards, and building your understanding of what good looks like in a modern ML engineering function.
Some of the great things you'll be doing:
ML Engineering and AI Systems
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Deliver well-scoped ML and AI system components end-to-end, from design through to testing, deployment, and production support
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Contribute hands-on code to model packaging, deployment, and lifecycle automation
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Build and improve systems that monitor model performance, drift, reliability, and operational health
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Support both batch and real-time ML workloads depending on use case requirements
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Work on emerging AI and LLM-powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value
Platform and Standards
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Contribute to the evolution of our internal ML and AI platform to support experimentation, governance, and collaboration
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Apply and help promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, model and agent observability and evaluation
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Contribute to shared tools and libraries that accelerate safe, efficient, and scalable ML development
Collaboration
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Work closely with data scientists to help productionise experimental models and turn prototypes into robust services
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Contribute to code reviews, providing and receiving constructive feedback
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Participate in architecture and design discussions, raising considerations around scalability, reliability, and system interactions
Culture and Innovation
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Contribute to a culture of engineering excellence, collaboration, and continuous learning
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Stay curious about emerging tools and approaches in MLOps and applied AI, and bring ideas for improvement to the team
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Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems
What we'd like to see from you:
The Essentials
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Some experience delivering ML or AI components in a production or near-production environment
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Solid software engineering skills in Python, with an understanding of how to write maintainable, testable code
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Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent
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Familiarity with CI/CD pipelines and how they apply to ML systems
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An understanding of ML lifecycle challenges - versioning, testing, monitoring, governance
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Good collaboration and communication skills, with an ability to work effectively across data science, engineering, and product teams
Nice to Have
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Exposure to LLM-based systems - for example prompt engineering, RAG, or orchestration frameworks such as LangGraph or LangChain
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Familiarity with multi-step AI patterns - systems where models plan, retrieve information, and take sequences of actions
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Active personal use of AI-assisted or agentic coding tools, and an interest in how similar patterns could be applied to automate and accelerate ML engineering workflows - candidates who are exploring this space, even at an early stage, are encouraged to talk about it
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Experience or interest in financial services, insurance, or another regulated sector
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Interest in responsible AI and ML model governance
Why Compare the Market?
We’re a business built for pace and performance. Here, you’ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
If you’re ready to stretch yourself, raise the bar, and grow with a team that’s serious about performance, innovation, and purpose, we’d love to hear from you.
