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.
Skills Required
- Commercial production machine learning experience at high data volume, including deployment, CI/CD, monitoring, and lifecycle management
- Strong Python skills with PyTorch or a similar machine learning framework
- Ability to evaluate model performance, diagnose issues across data, features, and architecture, and make engineering trade-offs
- Knowledge of real-time machine learning inference patterns and reproducible environments
- Experience with Docker and MLflow or equivalent tools
- Experience with AWS, SageMaker, Snowflake, Spark or Databricks, and Kubernetes
- Engineering focus on reliability, maintainability, and continuous improvement
Global Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Global and has not been reviewed or approved by Global.
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Leave & Time Off Breadth — 25 days’ annual leave plus an extra work‑anniversary day, with the option to buy up to five additional days, points to broad time‑off flexibility. Enhanced family and accessibility leave further expand coverage.
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Healthcare Strength — Access to a 24/7 virtual GP, health and dental discount programs, long‑term sickness insurance, and life assurance at 4x salary indicate robust health protection. Free Headspace membership and nutrition coaching complement the core offering.
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Wellbeing & Lifestyle Benefits — Discounted gym memberships, cashback benefits, discounted travel insurance, and access to company‑sponsored music events contribute lifestyle value. Feedback suggests these extras add to a fun, wellbeing‑oriented culture.
Global Insights
What We Do
The UK and Europe’s largest Radio & Outdoor company, Global is home to respected, national market-leading media brands broadcasting across the UK on DAB & FM and around the world on Global Player, including Heart, Capital, LBC, Capital XTRA, Capital Dance, Classic FM, Smooth, Radio X and Gold. Global Player allows listeners to enjoy all of Global’s radio brands, award-winning podcasts, and expertly curated playlists, in one place in app, on web and on smart speakers. Global is also one of the leading Outdoor companies in both the UK & Europe, with over 253,000 sites reaching 95% of the UK population. Global’s extensive and diverse outdoor portfolio encompasses Transport for London’s Underground network, almost all major UK airports including Gatwick, the UK’s largest portfolio of roadside posters and premium digital screens in prime locations, as well as the UK’s largest network of buses including all major cities. On-air, on Global Player and with our outdoor platforms combined, Global reaches 51 million individuals across the UK every week, including 26.3 million on the radio alone.
