Senior Machine Learning Engineer

Are you ready to transform the insurance industry?

Policy Expert is a forward-thinking business that loves to get things done. Leveraging proprietary technology and smart data, we offer reliable products and a wow customer experience.

Having achieved rapid growth since being founded in 2011, we've won over 1.5 million customers in Home, Motor and Pet insurance and have been ranked the UK's No.1-rated home insurer by Review Centre since 2013.

We are seeking a Senior Machine Learning Engineer to play a leading role in the design and evolution of Policy Expert's next-generation ML platform on Google Cloud.
You'll work as a hands on technical expert in building reusable, scalable, and observable ML infrastructure that empowers data scientists and product teams to deliver measurable business impact. This is a high-impact individual contributor role for an engineer who enjoys coding, automation, and bringing order to complex DS/ML ecosystems.

• Design, implement and standardise end-to-end machine learning pipelines using Vertex AI Pipelines, Model Registry, and Cloud Run, with a strong focus on reliability, automation, and cost efficiency.
• Build reusable components and templates to accelerate model delivery across squads (training, evaluation, registry, monitoring).
• Develop MLOps frameworks and SDKs around metadata tracking, feature versioning, model governance, and CI/CD integration (e.g. Cloud Build, Terraform, GitHub Actions).
• Partner with data scientists and pricing analysts to translate model prototypes into fully automated, monitored deployments.
• Optimise data processing and orchestration using BigQuery, Dataflow, and cloud-native patterns (Container, Cloud Composer, Pub/Sub).
• Support platform adoption by mentoring ML engineers and data scientists, and contributing to shared documentation, examples, and tooling.
• Mentor and upskill peers in engineering excellence, code quality, and platform use.
• Stay close to emerging trends in ML systems, generative AI, and agents; evaluating their fit within the MLOps landscape.

• A degree in Computer Science, Software Engineering, Data Science, or another quantitative field.
• 4+ years of experience building and deploying production ML systems, with significant time spent on GCP.
• Deep, hands on experience with Vertex AI (Pipelines, Model Registry, Experiments, Model Monitoring) and GCP services such as BigQuery, Cloud Storage, and Cloud Run.
• Proven track record of designing MLOps or ML platform tooling, not just consuming it (e.g. custom pipeline components, SDKs, or frameworks).