We Are Global 

At Global, we think big, work hard, and never stand still. We're home to some of the UK's biggest and best-loved radio brands, powerful Outdoor advertising, and world-class technology - all driven by talented people who care deeply about what they do. 

Our mission is to make everyone's day brighter: our audiences, our customers, our communities, and each other. And whether we're on air, outdoors, or behind the scenes, we do it together. 

The Role 

We're looking for a Senior Machine Learning Engineer to join Global's Data team. 

You'll play a key role in building, deploying, and scaling machine learning solutions turning data science ideas into robust, production-grade products. You’ll support use cases across DAX, Global’s digital ad exchange platform, such as our ‘cross-device’ audience identity graph and algorithms to deliver real-time targeting across our audience

This role is ideal for someone who combines strong engineering fundamentals with hands-on machine learning experience, and who enjoys taking models from experimentation through to production in a cloud-based environment. 

The role reports into Global’s Head of Data Science. To support DAX use cases, you’ll be part of a high-performing, cross-functional squad of data engineers, product specialists and analytics experts who are passionate about using data to solve meaningful problems. Working closely with other DAX squads across the Technology department, you’ll help build and evolve our cutting-edge ad-serving technology for audio and outdoor. 

This is a hybrid role, with on-site days based at our Holborn office in Central London. 

Key Responsibilities 

• Design, build, and optimise machine learning and deep learning models, including for ad targeting and attribution, with a focus on scalability, performance, and accuracy 

• Build 

• Develop and support real-time inference systems with low latency and high throughput 

• Partner with data engineers to integrate ML workflows into wider data platforms and infrastructure, including Spark and Databricks 

• Implement model monitoring, drift detection, alerting, and retraining strategies 

• Optimise models for reliability and cost efficiency in AWS 

• Prototype and evaluate new and existing machine learning approaches to support Global's data products and use cases 

• Share best practice and mentor other technical professionals in production ML engineering 
   

What You'll Love About This Role 

• Think Big: Build ML and AI solutions that can 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 this role, success means: 

• You build machine learning products that deliver measurable value to the business and significantly improve Global’s capabilities in areas such as ad targeting and attribution 

• You ensure ML models are reliably deployed, monitored, and maintained in production, and ML pipelines are automated, reproducible, and scalable 

• You build real-time systems that operate efficiently and reliably under production demand 

• You have developed a strong understanding of Global's data ecosystem, tools, and operating model, particularly within DAX 

• You become a trusted technical contributor within the team and support others through coaching and best practice

What You'll Need: Essential Skills and Experience 

• Strong experience delivering machine learning & deep learning projects with high data volumes in a commercial environment 

• Hands-on experience translating business problems into ML algorithms, and iterating through training, tuning, and evaluation to address them 

• Experience evaluating ML models to diagnose why they may be underperforming - across data, features, and model architecture – and making reasoned trade-offs about what to change 

• Experience operating ML in production, including version control, model deployment, CI/CD, monitoring, and lifecycle management 

• Strong Python skills and experience with PyTorch or similar machine learning frameworks 

• Experience creating & 

• Experience with MLflow or equivalent tooling 

• Experience with Spark and distributed data processing 

• Strong understanding of real-time ML systems and production inference patterns 

• A strong engineering mindset, with a focus on reliability, maintainability, and continuous improvement 
   

Desirable 

• Experience working with LLMs, RAG, or GenAI systems 

• Experience using AI-assisted tools such as Claude Code to accelerate delivery, where appropriate 

• Exposure to vector databases and semantic search 

• Working knowledge of core data engineering concepts 

• Experience with recommendation systems, forecasting, or other real-time ML applications 
   

Tech Stack 

Cloud: AWS