Senior Machine Learning Engineer

Global; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Job summary

Global’s Data team is seeking a Senior Machine Learning Engineer to build, deploy and scale ML solutions—turning data science ideas into robust, production-grade products. You’ll support use cases across DAX, our digital ad exchange—such as real-time targeting and identity graphs—and join a cross-functional squad to evolve ad-serving technology for audio and Outdoor.

This hybrid role is based at our Holborn office in central London, offering collaboration with data engineers, product specialists

Qualifications

• Production ML projects with deployment, CI/CD, monitoring and lifecycle management.
• Strong Python (PyTorch or similar).
• Diagnose why models underperform across data, features and architecture.
• Production inference patterns, Spark and distributed data processing.
• Reproducible environments with Docker and MLflow on AWS.
• Engineering mindset focused on reliability and maintainability.

Responsibilities

• Model development and optimisation for scalable, production-grade ML and deep-learning models.

Skills

Production ML experience

Python (PyTorch)

Model evaluation

Real-time inference

Spark

Engineering mindset

Tools

Spark

Databricks

Snowflake

Docker

MLflow

AWS

Job description

Job Description

Senior Machine Learning Engineer

Accepting Applications Until: 25 September 2026

Your New Role

Global’s Data team is looking for a Senior Machine Learning Engineer to build, deploy and scale machine learning solutions—turning data science ideas into robust, production-grade products.

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.

What You’ll Need

• 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.