ML Engineer

In this role, you bridge data science and production by turning ML models into robust, scalable systems. You collaborate with Data Science, MLOps, Data Engineering and Product to ship models that power audience targeting, advertising measurement and content intelligence. You’ll own productionisation, monitoring and lifecycle management, driving reliable, reusable patterns. This position sits in Global’s central Data Science function in London, with opportunities to influence platform direction and cross-functional ways of working. You’ll contribute to impactful, data-driven products used by millions of listeners and brands.

• Productionise models and build serving infrastructure for batch and real-time use cases
• Maintain feature pipelines, model health, drift monitoring and periodic retraining
• Contribute to engineering standards, templates and documentation to enable data scientists to work independently
• Collaborate with Data Science, MLOps, Data Engineering and Product to ensure deployable, maintainable models

• Production ML experience with deployment, CI/CD and lifecycle management
• Strong Python with production-grade practices (unit tests, version control, defensive coding)
• Experience with PyTorch or similar ML frameworks (a plus)
• Model evaluation, monitoring and drift detection; ability to explain trade-offs
• Familiarity with workflow orchestration tools (e.g., Airflow)
• Understanding of production inference, reproducible environments (Docker, MLflow)
• Cloud experience with SQL data warehouses and platforms (AWS, GCP, Azure); AWS SageMaker, Snowflake, Kubernetes a plus
• Engineering mindset focused on reliability and continuous improvement

• ownership
• collaboration
• problem-solving
• Python (production-grade)
• PyTorch or similar frameworks
• CI/CD for ML