Key Notes
Production machine learning spans search, recommendations, pricing, routing, personalised experiences, digital marketing and AI-assisted customer support. The engineer owns delivery from data exploration and feature engineering through evaluation, deployment and maintenance, while building shared tools and frameworks. Relevant expertise includes Python, Spark, DevOps, MLOps and CI/CD knowledge; transport, GIS, cloud, large-language-model and graph experience are optional advantages.
What You'll Work On
- Design production machine-learning models and AI solutions for Trainline search, recommendations, pricing, routing, personalisation, marketing or customer-support products.
- Own delivery from data exploration and feature engineering through model selection, evaluation, deployment and ongoing maintenance.
- Build reusable tools, frameworks and libraries that improve machine-learning and AI delivery workflows across Trainline teams.
- Develop data products with Data Scientists, Software Engineers, Data Engineers, Machine Learning Engineers and Product Managers.
- Contribute to Trainline’s AI and ML community through technical learning and experimentation.
Why This Role Matters
- Trainline’s mobile and web search, recommendations, pricing and routing products use production machine-learning models to determine which journey options and fares customers encounter.
- Shared ML tools, frameworks and libraries give Trainline product teams reusable components for recurring development tasks, helping production-model delivery build on work already completed elsewhere.
What They Are Looking For
- Qualification: An advanced degree in Computer Science, Mathematics, Statistics or a similar quantitative discipline is sought.
- Technical: Python proficiency, including libraries such as Pandas, NumPy and Scikit-learn, is sought.
- Experience: Productionising ML/AI solutions and expertise in one of predictive modelling, classification, regression, optimisation, NLP or recommendation systems are sought.
- Technical: Spark, DevOps tools such as Docker/Terraform, MLOps such as MLflow, Agile and CI/CD experience or knowledge are sought.
- Preferred: Transport/GIS, cloud infrastructure, LLM fine-tuning, RAG or agents, and graph technology/algorithms are advantageous.