Your New Role: Senior Machine Learning Engineer

Global’s central Data Science function is recruiting a Senior Machine Learning Engineer to work across the full breadth of our data and product portfolio.

As a Senior Machine Learning Engineer at Global, you’ll be the engineering bridge between data science and production—taking models built by our data scientists and making them robust, scalable and maintainable across audience targeting, advertising measurement and content intelligence. It’s based in central London (Holborn, with occasional travel to Leicester Square).

Key Responsibilities

• Model Development, Productionisation & Migration (65%): Translate experimental models into reliable, testable production systems; build APIs and serving infrastructure across batch and real-time; design consistent feature pipelines; and audit and migrate existing models to modern standards without disrupting live products.

• Standards & Enablement (20%): Define engineering standards for how models are built, tested and deployed, aligned to the MLOps platform, and create reusable templates and documentation that help data scientists work independently.

• Cross-functional Partnership (15%): Work with data scientists, MLOps, data engineering and product to shape new products early and ensure models are handed off in a deployable, maintainable form.

What You’ll Love About This Role

• Think Big: This is a true AI and data-driven space—the models you ship influence what millions of listeners hear and how brands invest their media budgets.

• Own It: You’ll shape how we build going forward, not just maintain what exists—your engineering standards become the team’s standards.

• Keep it Simple: You’ll build reusable patterns and templates rather than one-off solutions.

• Better Together: You’ll work across the full range of Global’s data products, partnering with Data Science, MLOps, Data Engineering and Product.

What Success Looks Like

In your first few months, you’ll have:

• Built a clear understanding of Global’s engineering and data science tools, how they connect, and where ML sits commercially.

• Established strong working relationships and operating models with Data Science and MLOps.

• Completed an audit of existing ML models in production, assessing stability, maintainability and risk.

• Engineered a model to a more robust, documented state and built reusable components others can use.

What You’ll Need

• Production ML experience: You’ve delivered ML and deep-learning projects at high data volume in commercial environments, owning deployment, CI/CD, monitoring and lifecycle management.

• Strong Python: Solid Python with PyTorch or similar ML frameworks.

• Model evaluation: You diagnose why models underperform across data, features and architecture, and make reasoned trade-offs.

• Real-time ML & reproducibility: A strong grasp of production inference patterns and reproducible environments (Docker, MLflow or equivalent).

• Cloud & tooling: AWS, plus SageMaker, Snowflake, Spark/Databricks and Kubernetes.

• Engineering mindset: A focus on reliability, maintainability and continuous improvement.