Key Notes
Technical direction and complete production ownership distinguish this senior machine-learning role. The engineer makes architectural and modelling decisions, develops models and reusable tools, influences stakeholders and mentors less-experienced engineers without formal people management. Trainline’s hybrid model requires at least 60% office attendance over twelve weeks. Relevant expertise includes advanced quantitative training, production ML experience, Python, Spark, CI/CD and MLOps capability.
What You'll Work On
- Design machine-learning models for Trainline search, recommendations, pricing, routing, personalisation, marketing or AI-assisted customer-support products across mobile and web channels.
- Own data exploration, feature engineering, model selection, tuning, evaluation, deployment and maintenance across the production lifecycle.
- Set architectural and modelling direction for the assigned area, making decisions that must operate at Trainline scale.
- Build shared tools, frameworks and libraries that accelerate machine-learning delivery and improve engineering workflows across product teams.
- Mentor less-experienced engineers and influence technical and non-technical stakeholders during design and delivery decisions without holding formal people-management responsibility.
Why This Role Matters
- Trainline’s search, recommendations, pricing and routing products use production machine-learning models whose architecture and modelling decisions must work at the scale of the platform.
- Trainline’s ML product teams use shared frameworks and senior technical guidance to accelerate delivery workflows and develop less-experienced engineers’ capability.
What They Are Looking For
- Qualification: An advanced degree in Computer Science, Mathematics or a related quantitative discipline, or equivalent experience, is sought.
- Experience: Considerable production ML experience, with strength in an area such as predictive modelling, classification, regression, optimisation or recommendations, is sought.
- Technical: Strong Python with Pandas, NumPy and Scikit-learn, statistical methods, data extraction/manipulation and feature engineering are sought.
- Technical: Spark, Agile and CI/CD experience, plus DevOps/MLOps familiarity such as Docker, Terraform and MLflow, are sought.
- Preferred: Cloud, NLP/LLM fine-tuning, RAG or agents, graph technology, transport-sector or GIS exposure is ideal.