Machine Learning Engineering Manager

Overview

In this role you will lead a cross-functional ML team to deliver scalable AI-powered solutions that enhance Trainline’s platform and customer experience. You will own the end-to-end ML delivery lifecycle, from data exploration to deployment, while partnering with product and engineering to create data-driven products. You’ll shape the ML and AI strategy within a fast-growing, sustainability-driven company and mentor a diverse group of engineers, scientists and managers.

Pay / Benefits

• private healthcare
• dental insurance
• work from abroad policy
• 2-for-1 share purchase plans
• EV scheme
• family-friendly benefits

Responsibilities

• Lead a high-performing ML Engineering team alongside Software Engineers, Data Scientists, Data Engineers and Product Managers
• Deliver high-quality machine learning models and AI systems at scale with measurable business impact
• Own end-to-end ML lifecycle: data exploration, feature engineering, model selection, offline/online evaluation, deployment and maintenance
• Collaborate with stakeholders to propose innovative data products leveraging Trainline datasets and algorithms
• Foster a culture of learning, experimentation and knowledge sharing within AI/ML communities

Key requirements

• Advanced degree in Computer Science, Mathematics or a similar quantitative discipline
• Leadership experience either through management or mentorship
• Proficiency in Python with libraries (Pandas, NumPy, Scikit-learn)
• Experience productionising machine learning models
• Expertise in at least one area: predictive modelling, classification, regression, optimisation or recommendation systems
• Experience with Spark
• Knowledge of DevOps tools (Docker, Terraform) and MLOps practices (MLflow)
• Experience with agile delivery, CI/CD processes and tools
• Broad understanding of data extraction, manipulation and feature engineering techniques
• Familiarity with statistical methodologies
• Strong communication skills

• leadership
• clear communication
• collaboration
• Python and ML/DS libraries (scikit-learn, numpy, LightGBM, Pandas)
• PySpark
• AWS cloud infra (EMR, ECS, Athena)

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