Location
London

Hours
Full Time

Salary
Negotiable

About the Role
ATG Entertainment is seeking a Machine Learning Engineer to leverage our extensive data from millions of guests visiting our theatres annually. This role focuses on enhancing revenue, customer satisfaction, retention, and acquisition by delivering production-ready machine learning solutions and actionable insights from customer data. You will join a small, growing ML team at a pivotal time, taking ownership of defined parts of our ML estate and expanding your responsibilities as you grow.

Our Data Platform integrates 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 across the UK, USA, and Europe.

Working alongside other ML Engineers, you will own defined workstreams such as regional pipelines, monitoring layers, or model components, progressively managing the end-to-end lifecycle: data ingestion, model training and evaluation, deployment, pricing recommendations, and production monitoring.

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

Collaboration is key, as you will work closely with Data Science, Technology, Product, and commercial teams to bridge the gap between models, AI systems, and real business decisions.

Key Responsibilities
- Maximise value from data by building and iterating machine learning models, propensity models, and audience segmentations.
- Take ownership of components of the revenue management application, ensuring reliability and ongoing improvement.
- Productionise and automate ML models end-to-end including data ingestion, feature engineering, model training, 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 diagnosis and resolution of production issues, including on-call triage of failed pipeline runs and data quality problems.
- Design and implement monitoring for model outputs (data drift, prediction drift, anomaly detection) and build dashboards to track model behaviour and recommendation quality.
- Implement guardrails for pricing recommendations in line with agreed designs.
- Write well-tested, documented, maintainable Python and SQL code, following team conventions and participating in peer code review.
- Collaborate with Data Science to transition experimental models into production and with commercial teams to translate business needs into technical requirements.
- Document systems, runbooks, and model behaviour to share knowledge across the team.
- Contribute to the design and build of LLM-powered solutions including prompt management, versioning, and evaluation.
- Use the data platform to support and automate decision-making, improving understanding of products and customers.
- Actively participate in the data community, sharing learning and contributing to team standards.

Experience
- Proven experience as a machine learning engineer, data scientist, or similar role with a track record of delivering ML work into production.
- Experience building and optimising data science workloads in cloud environments (AWS preferred).
- Strong Python and SQL skills, including writing complex queries and well-structured, tested code.
- Experience deploying and maintaining ML models in production with a working understanding of MLOps (CI/CD, versioning, reproducibility).
- Experience building monitoring, alerting, and observability for data or ML systems.
- Understanding of system reliability concepts (failures, retries, idempotency, fault tolerance) and designing safeguards for automated decision systems.
- Ability to work effectively with data scientists, data engineers, and communicate clearly with technical and non-technical colleagues.
- Ability to manage own workload on defined projects, ask for help early, and take feedback and coaching.

About You
- Motivated to contribute to a growing ML team and take ownership of key components.
- Collaborative and able to work across technical and commercial teams.
- Interested in designing and implementing AI/LLM-powered solutions.
- Committed to maintaining high-quality, maintainable code and documentation.
- Open to learning and growing towards technical leadership and mentoring roles.

Desirable
- Prior experience in a similar industry (limited capacity and variable demand).
- Experience with pricing, revenue optimisation, or demand forecasting.
- Experience building monitoring dashboards (e.g. Streamlit, BI tools).
- Experience working with LLMs or generative AI systems in production or applied settings.
- Experience with Snowflake, Terraform, or SageMaker pipelines.
- Interest in growing towards technical leadership, including mentoring and owning a wider area of the platform.