Machine Learning Engineering Manager

About us At Trainline, our purpose is to empower greener travel choices, connecting people and places. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels.  Great journeys start with Trainline 🚄  We’re Europe’s leading independent rail platform, helping millions of travellers find and book the best-value rail and coach journeys across our app, website and partner channels. Our job is to make the green travel choice the best choice. By building a better train travel experience, we help more people choose rail - creating a positive impact for customers, our business and the planet. We’re a team of more than 1,000 Trainliners from over 50 nationalities, working across London, Paris, Barcelona, Milan, Edinburgh and Madrid. Now is a brilliant time to join us and help shape the future of travel. Introducing the Trainline Machine Learning and AI Team 👋 Machine learning is at the heart of Trainline’s mission to help millions of people make sustainable travel choices every day. Our models and AI agents support customers when journeys go wrong, power search and recommendations across our apps and website, find the best fares through pricing and routing optimisation, personalise the experience with generative AI, and make our digital marketing smarter. Our teams own the full lifecycle from idea to production, and work closely with colleagues across the business to grow the impact of ML and AI at Trainline. We’re looking for a Machine Learning Engineering Manager to lead a team of 7 ML engineers and data engineers within our Core Experience pillar, which covers Search, Supply and Commerce. Your team’s work sits at the centre of how customers find, compare and buy their tickets. You’ll be accountable for what your team ships and how it ships it: the quality of the systems, the pace of delivery and the growth of the people building them. You’ll work alongside data scientists, software engineers and product managers. You’ll stay close enough to the technical work to make good calls on architecture, trade-offs and risk. What unites us is curiosity, a love of what we do and a desire to make sustainable travel the easy choice. In this role, you will... 🚄 Lead, coach and grow a team of ML and data engineers. That means setting clear expectations, giving regular feedback, managing performance and building an inclusive team where people do their best work Hire for the team, shaping the roles we need and running a fair, rigorous interview process Own delivery: plan and prioritise with your product partners across Search, Supply and Commerce, unblock the team, and ship ML and AI systems that move measurable customer and business outcomes Set the technical direction for your team’s systems, including architecture, build-versus-buy, and knowing when a simpler approach beats a more sophisticated one Make sure the team builds well across the whole lifecycle: reliable data pipelines, high-quality features, rigorous offline and online evaluation (including A/B testing), reliable deployment, monitoring and ongoing maintenance Lead the team in building production-grade agentic systems, using orchestration harnesses such as LangGraph to design multi-step workflows, tool use and human-in-the-loop controls Make evaluation a first-class engineering discipline for LLM and agent work. That means curated eval sets, LLM-as-judge and human review, tracing, regression testing of prompts and agents, and online monitoring of quality, cost and latency Set the guardrails for responsible use of AI, covering safety, privacy and clear fallbacks when an agent shouldn’t act on its own Raise the bar on engineering practice through code and design reviews, CI/CD, MLOps, observability and on-call Build strong relationships with senior leaders and partner teams, explaining progress, trade-offs and impa