TrainlineJunior Machine Learning Engineer
Trainline is hiring a Junior Machine Learning Engineer in London, United Kingdom. It pays £40k-£50k a year and Level rates it ; you can apply on Level.
AI in this role
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 is at the heart of Trainline's mission to help millions of people make sustainable travel choices every day. Our ML models power critical aspects of our platform, including:
Advanced search and recommendations capabilities across our mobile and web applications
Pricing and routing optimisations to find the best fares for customers
Personalised user experiences enhanced by Reinforced Learning Agents & Generative AI
Data-driven digital marketing systems
AI agents improving customer support
Our machine learning teams own the complete delivery lifecycle from ideation to production. We work closely with stakeholders across the business to expand the understanding and impact of machine learning and AI throughout Trainline.
The Role:
We are looking for a Junior Machine Learning Engineer to join our team help shape the future of train travel. You will be part of a highly innovative AI and ML platform working alongside engineers, scientists and product managers to tackle complex challenges by combining Trainline’s rich datasets with cutting edge algorithms. What unites our team is an expertise in the field, a love of what we do and the desire to create impactful solutions to support Trainline’s goals of encouraging sustainable travel.
As a part of Trainline you will be joining an environment where learning and development is top priority. You will have the opportunity to work with fellow ML enthusiasts on large-scale production systems, delivering highly impactful products that make a difference to our millions of users.
As a Junior Machine Learning Engineer at Trainline you will... 🚄
Work in cross-functional teams combining data scientists, software, data and machine learning engineers, and product managers
Design and deliver machine learning models at scale that drive measurable impact for our business
Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance
Work with MLE Team and Stakeholders to help design and deliver data driven products using Trainline's extensive datasets and modern machine learning techniques
Create the tools, frameworks and libraries that enables the acceleration of our ML products delivery and improve our workflows
Take an active part in our AI and ML community and foster a culture of rigorous learning and experimentation
We'd love to hear from you if you...🔍
Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline
Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.)
Have experience productionising machine learning models
Are an expert in one of predictive modeling, classification, regression, optimisation or recommendation systems
Have experience with Spark
Have knowledge of DevOps technologies such as Docker and Terraform and ML Ops practices and platforms like ML Flow
Have experience with agile delivery methodologies and CI/CD processes and tools
Have a broad of understanding of data extraction, data manipulation and feature engineering techniques
Are familiar with statistical methodologies.
Have good communication skills
Nice to have 😍
Understanding and/or hands-on experience of Reinforcement Learning theories, frameworks, and algorithms
Experience with cloud infrastructure
Experience with Large Language Models (fine tuning, RAG, agents)
Experience with graph technology and/or algorithms, understanding of NLP algorithms
Our technology stack 💻
Python and associated ML/DS libraries (scikit-learn, NumPy, LightGBM, Pandas, TensorFlow, etc...)
PySpark
AWS cloud infrastructure: EMR, ECS, Athena, etc.
MLOp/DevOps: Terraform, Docker, Airflow, MLFlow, NewRelic
The interview process 🚉
Recruiter Call (30 minutes)
Meeting a Machine Learning Manager (30 minutes)
Technical Interview with 2 x Engineers (90 mins)
Final Interview with the Head of (30-45 mins)
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!
How we rate this
Junior Machine Learning Engineer at Trainline rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $198k
Use the typical machine learning engineer figure as a reference when pay comes up. See machine learning engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Fine-tuning, ML Ops, and NLP. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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