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 https://www.linkedin.com/company/trainline/, Instagram https://www.instagram.com/lifeattrainline/ and Glassdoor https://www.glassdoor.co.uk/Overview/Working-at-Trainline-EI_IE249203.11,20.htm!
