Machine Learning Engineer

ATG has a wealth of data, with millions of guests visiting, eating, and drinking in our theatres every year, and there is a growing need to bring machine learning and data-driven decision-making into our core business processes.

The focus of this role is to improve revenue, customer satisfaction, retention, and acquisition by delivering production-ready machine learning solutions and actionable insights from our customer data. You will join a small, growing ML team at a pivotal time for ATG, taking ownership of defined parts of our ML estate, and working towards wider ownership as you grow.

We have built a Data Platform that collates our rich transactional and behavioural data in a cloud-based environment (Snowflake and AWS), enabling scalable analytics and machine learning across the business.

You will contribute to the development, deployment, and continuous improvement of machine learning systems, including a revenue management application used by teams across the UK, USA, and Europe. Working alongside other ML Engineers, you will take ownership of defined work streams (for example, a regional pipeline, a monitoring layer, or a model component), progressively taking on more of the end-to-end lifecycle: data ingestion, model training and evaluation, deployment, pricing recommendations and production monitoring.

You will also have the opportunity to help design and implement AI/LLM-powered solutions, such as internal tools, decision-support systems, or workflow automation, ensuring they are well-evaluated, cost-effective, and integrated with our data platform.

You will work closely with Data Science, Technology, Product, and commercial teams, helping to bridge the gap between models, AI systems, and real business decisions.

• You will help us maximise the value we derive from our data, building and iterating machine learning models, propensity models, and audience segmentations. You will collaborate across technical and business teams to create assets that can be used and understood.
• Take ownership of defined components of the revenue management application (i.e. regional pipelines, seat map and pricing data pulls, or model inference), including their reliability and ongoing improvement.
• Product ionise and automate machine learning models end-to-end, including data ingestion, feature engineering, model training and evaluation, inference, and delivery of pricing recommendations.
• Build and maintain data pipelines and MLOps workflows (CI/CD, model versioning, reproducibility, runtime handling) in AWS and Snowflake.
• Support the diagnosis and resolution of production issues, including on-call style triage of failed pipeline runs and data quality problems, escalating to the Lead ML Engineer where needed.
• Design and implement monitoring for model outputs (data drift, prediction drift, anomaly detection) and build dashboards to track model behaviour and recommendation quality daily.
• Implement guardrails for pricing recommendations (thresholds, overrides, fallback logic) in line with agreed designs.
• Write well-tested, documented, maintainable Python and SQL, following team conventions and taking part in peer code review.
• Work with Data Science to take experimental models and prototypes into production, and with commercial teams to translate business needs into technical requirements.
• Document systems, runbooks, and model behaviour so that knowledge is shared across the team.
• Contribute to the design and build of LLM-powered solutions (e.g. internal tools, decision-support systems, automation workflows), including prompt management, versioning, and evaluation.
• Use our data platform to support and automate decision-making across the business, bringing data together to constantly improve our understanding of our products and customers.
• Be an active part of our data community as we build new capability across disciplines, sharing learning and contributing to team standards.

If you have most of the essential criteria, we encourage you to apply, and welcome transferable skills from other industries or backgrounds. We can give experience of any desirable criteria but may also use them to decide between candidates for this role.

• Proven experience as a machine learning engineer, data scientist, or similar role, with a track record of delivering ML work into production.
• Experience of building and optimising data science workloads in cloud environments (AWS…