Senior Machine Learning Engineer

Role overview

As a Senior Machine Learning Engineer on Trainline’s Machine Learning and AI Team, you’ll design and deliver ML models that power core parts of the platform—from search, recommendations, pricing, routing optimisation, to personalised experiences enhanced by generative AI and AI agents.

You’ll own the end-to-end ML delivery lifecycle and help shape technical direction, mentor peers, and influence stakeholders across the business.

Responsibilities

• Work in cross-functional teams with data scientists, software engineers, data engineers, and product managers.
• Design and deliver ML models at scale with measurable business impact.
• Own the end-to-end ML lifecycle: data exploration, feature engineering, model selection and tuning, evaluation, deployment, and maintenance.
• Make architectural and modelling decisions that hold up at scale.
• Partner with stakeholders to propose innovative data products using Trainline datasets and latest algorithms.
• Build tools/frameworks/libraries to accelerate ML product delivery and improve team workflows.
• Provide technical mentorship to less experienced engineers (no formal people management responsibility).
• Contribute to the AI/ML community by promoting rigorous learning and experimentation.

Requirements

• Advanced degree in Computer Science, Mathematics, or related quantitative field (or equivalent experience).
• Strong experience productionising ML models, with depth in areas such as:
  • predictive modelling, classification, regression, optimisation, or recommendation systems.
• Proficiency in Python and open-source data libraries such as Pandas, NumPy, and Scikit-learn.
• Solid grounding in statistical methodologies plus data extraction, manipulation, and feature engineering.
• Experience with Spark, Agile delivery methods, and CI/CD practices.
• Familiarity with DevOps/MLOps tools and practices including Docker, Terraform, and MLflow.
• Ability to influence and communicate with both technical and non-technical stakeholders.

Nice to have

• Exposure to cloud infrastructure
• Experience with NLP or large language models (e.g., fine-tuning).