Accepting applications until:

We Are Global

At Global, we think big, work hard, and never stand still.We'rehome 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 whetherwe'reon air, outdoors, or behind the scenes, we do it together.

The Role

We'relooking for a Senior Machine Learning Engineer to joinGlobal'sData team.

You'llplay a key role in building, deploying, and scaling machine learning solutionsturning data scienceideas into robust, production-grade products.You’llsupport use cases acrossDAX,Global’sdigital ad exchange platform,such as our ‘cross-device’ audience identity graph andalgorithms to deliver real-time targetingacross 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 intoGlobal’sHead of DataScience To support DAX use cases,you’llbe part of ahigh-performing, cross-functionalsquadof 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’llhelp build and evolve ourcutting-edgead-serving technology for audio and outdoor.

This isahybridrole, withon-site days based at ourHolbornofficein Central London

Key Responsibilities

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

• Buildandmaintainrobustend-to-end ML pipelines covering training, validation, deployment, and monitoring

• 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 modelsforreliabilityand cost efficiency in AWS

• Prototype and evaluate new and existing machine learning approaches to supportGlobal'sdata products and use cases

• Share best practice and mentor othertechnical professionals inproduction 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:

• Youbuildmachine learningproductsthat deliver measurable value to the business andsignificantlyimproveGlobal’scapabilities in areas such as ad targetingandattribution

• You ensureMLmodels are reliably deployed,monitored, andmaintainedin production, and MLpipelines are automated, reproducible, and scalable

• You build real-time systemsthatoperateefficiently and reliably under production demand

• Youhavedevelopedastrong understanding ofGlobal'sdata 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 experiencedeliveringmachine learning& deep learningprojectswith highdatavolumesina commercialenvironment

• Hands-on experiencetranslatingbusinessproblemsintoML algorithms,and iterating through training,tuning, and evaluationto 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

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

• Strong Pythonskills and experience withPyTorchor similar machine learning frameworks

• Experience creating &maintainingreproducible environments and familiarity with tools such as UV/docker

• Experience withMLflowor 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

• Experienceusing 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 withrecommendation systems, forecasting, or other real-time ML applications
  

Tech Stack

Cloud: AWS