Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Global, you’ll support use cases across DAX, our digital ad exchange-such as the cross-device audience identity graph and real-time targeting algorithms. You’ll join a high-performing, cross-functional DAX squad of data engineers, product specialists and analytics experts, helping build and evolve our cutting-edge ad-serving technology for audio and Outdoor. This is a hybrid role based at our Holborn office in central London. Key Responsibilities Model Development & Optimisation: Design, build and optimise ML and deep-learning models-including for ad targeting and attribution-with a focus on scalability, performance and accuracy, and prototype and evaluate new approaches.

ML Pipelines & Real-Time Inference: Build and maintain robust end-to-end ML pipelines covering training, validation, deployment and monitoring, and develop real-time inference systems with low latency and high throughput.

Monitoring & Reliability: Implement model monitoring, drift detection, alerting and retraining, and optimise models for reliability and cost efficiency in AWS.

Collaboration & Enablement: Partner with data engineers to integrate ML workflows into wider platforms (Spark, Databricks), and share best practice and mentor other technical professionals.

What You’ll Love About This Role Think Big: Build ML and AI solutions that shape products, improve decision-making and unlock growth.

Own It: Take ideas from concept to production and see the impact of your work in the real world.

Keep it Simple: Turn complex technical challenges into scalable, practical solutions.

Better Together: Work with smart, supportive people across data, engineering, analytics and the wider business.

What Success Looks Like In your first few months, you’ll have: Built ML products that deliver measurable value, improving Global’s capabilities in areas such as ad targeting and attribution.

Ensured ML models are reliably deployed, monitored and maintained, with automated, reproducible and scalable pipelines.

Built real-time systems that operate efficiently and reliably under production demand.

Developed a strong understanding of Global’s data ecosystem, tools and operating model, particularly within DAX.

Production ML experience: You’ve delivered ML and deep-learning projects at high data volume commercially, 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 & distributed ML: A strong grasp of production inference patterns, plus Spark and distributed data processing.

Reproducibility & tooling: Reproducible environments (UV/Docker) and MLflow or equivalent, on AWS with Spark, Databricks and Snowflake.

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