Head of Machine Learning Engineering

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 Machine Learning and AI at Trainline 👋

Machine learning and AI are central to how Trainline helps millions of customers make smarter, more sustainable travel choices every day. Our ML models and AI systems power critical parts of the platform - from search and recommendations to pricing intelligence, personalisation, digital marketing, and AI-driven customer support.

Our ML teams own the full delivery lifecycle, from ideation through to production. We work closely with stakeholders across the business to expand the reach and impact of ML and AI throughout Trainline.

The Role

This is a senior leadership role sitting within one of Trainline’s most important product areas: Core Experience. This pillar encompasses the products and systems closest to the customer journey - search, pricing, payments, fare intelligence, and conversational interfaces.

You will define and lead the ML strategy, team, and delivery across this domain. That means building and scaling ML and AI capabilities that measurably improve the customer experience and drive business outcomes. The work spans traditional ML systems and emerging agentic/LLM-based capabilities.

You will lead multiple ML teams through their respective team leads, reporting to the Director of AI & ML. You’ll be accountable for the people (2 ML Managers and ~10 ICs), delivery, and budget across your pillar - including vendor and infrastructure cost management. You’ll work closely with Product, Engineering, Data, Analytics, and commercial stakeholders, combining strategic thinking, technical leadership, and organisational leadership to ensure Trainline continues to ship high-quality, scalable ML systems in production.

As a Head of Machine Learning - Core Experience at Trainline you will... 🚄

Set direction and own the budget. Define the vision, strategy, and roadmap for ML across the Core Experience pillar. Own the pillar’s budget - including cloud infrastructure, vendor costs, and third-party tooling - balancing short-term delivery with longer-term platform investments.

Lead and grow the organisation. Lead multiple teams through their team leads. Coach managers and senior ICs, set hiring strategy, and build a culture of technical excellence, experimentation, and delivery. Partner closely with Product, Engineering, and commercial stakeholders to identify high-value opportunities and raise adoption of ML across the business.

Deliver and operate. Drive delivery across search relevance, payments models, splits and fare optimisation, supply automation, and conversational AI. Ensure your teams ship end-to-end - from prototyping through deployment, monitoring, and iteration. Raise operational maturity by defining SLIs/SLOs, incident management, and on-call practices for ML systems in production.

Shape AI practices and governance. Co-own the operating model for modern AI at Trainline - LLM-based features, agentic systems, evaluation frameworks, and guardrails. Drive improvements in AI governance, data privacy, security, and audit accountability as capabilities scale.

We'd love to hear from you if you have...🔍

Significant experience building and leading production ML teams, including managing through team leads (manager-of-managers)

Strong track record of shipping ML systems that deliver measurable business or customer impact, across problem types such as ranking, recommendation, forecasting, optimisation, or real-time decisioning

Deep understanding of the full ML lifecycle and strong software/platform instincts - you know what it takes to run reliable ML at scale – including MLOps tooling and practices

Experience raising operational standards: SLIs/SLOs, monitoring, incident management, and production reliability

Budget ownership, including infrastructure cost management and vendor oversight