Senior Machine Learning Engineer

Overview

In this role you will design and deploy ML models at scale to drive measurable business impact within Trainline’s cross-functional teams. You will own the end-to-end ML lifecycle, from data exploration to deployment, shaping technical direction and influencing stakeholders. You’ll build tools and frameworks to accelerate ML delivery and mentor engineers, contributing to Trainline’s AI/ML community. This is a chance to work on high-impact models powering search, pricing, and personalized experiences in a sustainability-focused travel platform.

Pay / Benefits

• Private healthcare & dental insurance
• Generous work from abroad policy
• 2-for-1 share purchase plans
• EV scheme to reduce carbon emissions
• Extra festive time off
• Family-friendly benefits

Responsibilities

• Collaborate with data scientists, software engineers, data engineers and product managers in cross-functional teams
• Design and deliver scalable ML models that drive measurable business impact
• Own end-to-end ML delivery lifecycle: data exploration, feature engineering, model selection, evaluation, deployment and maintenance
• Shape technical direction with scalable architecture and modeling decisions
• Partner with stakeholders to propose data products leveraging Trainline datasets and algorithms
• Build tools, libraries and frameworks to speed up ML delivery and improve team workflows
• Provide technical mentorship to less experienced engineers without people management
• Actively contribute to the AI/ML community to foster rigorous learning and experimentation

Key requirements

• Advanced degree in Computer Science, Mathematics or related quantitative discipline, or equivalent experience
• Experience productionising machine learning models in predictive modelling, classification, regression, optimisation or recommendations
• Strong Python proficiency with Pandas, NumPy and Scikit-learn
• Solid grounding in statistics and data manipulation/feature engineering
• Experience with Spark, agile delivery, and CI/CD practices
• Familiarity with DevOps and MLOps tools such as Docker, Terraform and MLFlow
• Confident in influencing and communicating with diverse stakeholders across teams
• Ideally exposure to cloud infra, NLP/LLMs (e.g., fine-tuning, RAG), graph tech, or GIS; transport sector or GIS experience
• Location: London
• Employment Type: Full time
• Location Type: Hybrid

• Stakeholder influence
• Cross-functional collaboration
• Mentoring and supporting peers
• Python, Pandas, NumPy, Scikit-learn
• Spark
• CI/CD

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