Senior Machine Learning Engineer

External

Thisisglobal · Holborn - London

Full-timeHybrid5d ago

AWSCI/CDDeep LearningDockerLLMsMachine Learning

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About 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, y ou'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 Holbor n office in Central London .

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 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

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 m achine 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 r eal-time systems that operate efficiently and reliably under production demand

You have develop ed 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 problem s into ML algorithm s , 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 skill s and e xperience with PyTorch or similar machine learning frameworks

Experience creating & 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

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

Senior Machine Learning Engineer

About the role

Responsibilities

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