London · hace 3 semanas
Liderar y hacer crecer a un equipo de siete ingenieros de ML y datos en el pilar Core Experience, asumiendo la entrega de sistemas de ML y agentes en búsqueda, oferta y comercio. Definir la dirección técnica, las prácticas de evaluación y la contratación del equipo.
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 impact in plain language • Contribute to our wider AI and ML community and help keep a culture of learning, curiosity and experimentation We’d love to hear from you if you have... 🔎 • Experience line-managing engineers, including hiring, performance management and career development • A strong background as an ML engineer, with a track record of taking models into production and keeping them healthy at scale • Enough grounding in data engineering (pipelines, data modelling and data quality) to lead data engineers as well as ML engineers • Solid Python and software engineering fundamentals, with enough hands-on depth to review code and designs credibly • Depth in at least one area such as ranking and recommendation, predictive modelling, optimisation or NLP, backed by a solid grasp of statistics, experimentation and feature engineering • Experience with modern ML infrastructure on a major cloud platform, ideally AWS, covering distributed data processing, orchestration, model management and infrastructure as code • Hands-on experience shipping agentic or LLM-based systems, including agent orchestration frameworks, tool use, RAG, and fine-tuning where it earns its place • A disciplined approach to evaluating LLM and agent systems, using tools such as Braintrust, and a clear view on how to know whether an agent is working well in production • Clear communication with technical and non-technical colleagues, and good judgement on priorities when there’s more to do than people to do it Our tech stack 💻 We build mostly in Python on AWS, and choose tools for what they do in production rather than for novelty. You don’t need to know all of it on day one. • Data and compute: S3, Athena, Spark on EMR, SQL • Orchestration and MLOps: Airflow, MLflow, Docker, ECS, Terraform, GitHub Actions for CI/CD • Models: foundation models served through Amazon Bedrock • Agents: LangGraph for orchestration, deployed and run on Amazon Bedrock AgentCore, with MCP for tool integration • Evaluation: Braintrust for evals, tracing and monitoring agent quality in production More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions, extra festive time off, and excellent family-friendly benefits. We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets, and regular learning days. Jump on board and supercharge your career from day one! We're operating a hybrid model and ask that Trainliners work from the office a minimum of 60% of their time over a 12-week period. We also have a 28-day Work from Abroad policy. Our values represent the things that matter most to us and what we live and breathe everyday, in everything we do: • 💭 Think Big - We're building the future of rail • ✔️ Own It - We focus on every customer, partner and journey • 🤝 Travel Together - We're one team • ♻️ Do Good - We make a positive impact We know that having a diverse team makes us better and helps us succeed. And we mean all forms of diversity - gender, ethnicity, sexuality, disability, nationality and diversity of thought. That's why we're committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated. Interested in finding out more about what it's like to work at Trainline? Why not check us out on LinkedIn, Instagram and Glassdoor!
Publicada el 15 oct 2026
