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 that turn 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 that 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 to Global’s Head of Data Science and works within a high‑performing, cross‑functional squad of data engineers, product specialists and analytics experts. 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 at the Holborn office in Central London.
Key Responsibilities
- Design, build and optimise machine learning and deep learning models for ad targeting and attribution, focusing on scalability, performance and accuracy.
- Build and maintain robust end‑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 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
- Build ML and AI solutions that shape products, improve decision‑making and unlock growth.
- Take ideas from concept to production and see the impact of your work in the real world.
- Turn complex technical challenges into scalable, practical solutions.
- Collaborate with smart, supportive people across data, engineering, analytics and the wider business.
What Success Looks Like
- Build machine learning products that deliver measurable value to the business and significantly improve Global’s capabilities in ad targeting and attribution.
- Ensure ML models are reliably deployed, monitored and maintained in production, and that ML pipelines are automated, reproducible and scalable.
- Build real‑time systems that operate efficiently and reliably under production demand.
- Develop a strong understanding of Global’s data ecosystem, tools and operating model, particularly within DAX.
- 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 and 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 underperformance across data, features and architecture and making reasoned trade‑offs.
- 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 and maintaining reproducible environments and familiarity with tools such as UV/docker.
- Experience with MLflow or equivalent tooling.
- Experience with Spark and distributed data processing.
- Strong understanding of real‑time ML systems and production inference patterns.
- Strong engineering mindset, focusing 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
- Machine Learning: PyTorch, Spark ML
- MLOps: MLflow or equivalent
- Data Platforms: Spark, Databricks, Snowflake
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