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- Tasks: Join our team to build innovative AI and ML products for sustainable travel.
- Company: Trainline is Europe's number 1 rail app, dedicated to making travel simple and affordable.
- Benefits: Enjoy perks like private healthcare, work-from-abroad options, and generous learning budgets.
- Why this job: Be part of a mission-driven team that values learning, collaboration, and impactful solutions.
- Qualifications: Advanced degree in Computer Science or related field; experience with NLP and machine learning required.
- Other info: Diversity is key at Trainline; we celebrate all forms of diversity in our workplace.
The predicted salary is between 43200 - 72000 £ per year.
About us: We are champions of rail, inspired to build a greener, more sustainable future of travel. 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. Now Europe’s number 1 downloaded rail app, with over 125 million monthly visits and £5.9 billion in annual ticket sales, we collaborate with 270+ rail and coach companies in over 40 countries. We want to create a world where travel is as simple, seamless, and affordable as it should be. Today, we're a FTSE 250 company driven by our incredible team of over 1,000 Trainliners from 50+ nationalities, based across London, Paris, Barcelona, Milan, Edinburgh and Madrid. With our focus on growth in the UK and Europe, now is the perfect time to join us on this high-speed journey.
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 AI systems power critical aspects of our platform, including:
- AI agents improving customer support and changing how we travel
- 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 generative AI
- Data-driven digital marketing systems
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. We are looking for a Machine Learning Engineer to join the Product ML team to help shape the future of train travel. You will build highly innovative AI and ML products working alongside engineers, scientists and product managers to tackle complex challenges by combining Trainline’s rich data sets 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 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 NLP based machine learning systems 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
- Partner with stakeholders to propose innovative data products that leverage Trainline’s extensive datasets and state of the art algorithms
- Create the tools, frameworks and libraries that enable 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
- Understanding of NLP algorithms and techniques and/or experience with Large Language Models (fine tuning, RAG, agents)
- 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 understanding of data extraction, data manipulation and feature engineering techniques
- Are familiar with statistical methodologies
- Have good communication skills
Nice to have:
- Experience with LangGraph or LangChain
- Experience with transport industry and/or geographical information systems (GIS)
- Experience with cloud infrastructure
- Experience with graph technology and/or algorithms
Our technology stack:
- Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc...)
- PySpark
- AWS cloud infrastructure: EMR, ECS, Athena, etc.
- MLOps: Terraform, Docker, Airflow, MLFlow
More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, 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!
Our values represent the things that matter most to us and what we live and breathe every day, 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.
Machine Learning Engineer FullTime London employer: Trainline plc
Contact Detail:
Trainline plc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer FullTime London
✨Tip Number 1
Familiarise yourself with Trainline's mission and values. Understanding their commitment to sustainable travel and how machine learning plays a role in this can help you align your discussions during interviews, showcasing your passion for their goals.
✨Tip Number 2
Engage with the Machine Learning community on platforms like LinkedIn or GitHub. Sharing your projects or insights related to NLP and machine learning can demonstrate your expertise and enthusiasm, making you stand out as a candidate.
✨Tip Number 3
Prepare to discuss specific machine learning projects you've worked on, especially those involving NLP or large datasets. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving skills.
✨Tip Number 4
Network with current or former employees of Trainline. They can provide valuable insights into the company culture and the expectations for the Machine Learning Engineer role, which can help you tailor your approach during the application process.
We think you need these skills to ace Machine Learning Engineer FullTime London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience and skills that align with the Machine Learning Engineer role. Focus on your proficiency in Python, NLP algorithms, and any experience with large language models or productionising machine learning models.
Craft a Compelling Cover Letter: In your cover letter, express your passion for sustainable travel and how your background in machine learning can contribute to Trainline's mission. Mention specific projects or experiences that demonstrate your expertise and enthusiasm for the role.
Showcase Your Projects: If you have worked on relevant projects, include links to your GitHub or portfolio. Highlight any machine learning systems you've designed or contributed to, especially those involving NLP or data-driven solutions.
Prepare for Technical Questions: Anticipate technical questions related to machine learning concepts, algorithms, and tools mentioned in the job description. Brush up on your knowledge of statistical methodologies, feature engineering, and DevOps practices to impress during the interview process.
How to prepare for a job interview at Trainline plc
✨Understand the Company’s Mission
Before your interview, make sure you understand Trainline's mission to promote sustainable travel. Familiarise yourself with their products and how machine learning plays a role in enhancing user experiences. This will show your genuine interest in the company and its goals.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and relevant ML libraries like Scikit-learn and TensorFlow. Highlight any projects where you've implemented NLP algorithms or worked with large datasets, as this aligns closely with the role's requirements.
✨Prepare for Problem-Solving Questions
Expect to face technical questions that assess your problem-solving abilities. Practice explaining your thought process when tackling machine learning challenges, such as model selection and tuning, to demonstrate your expertise and analytical skills.
✨Emphasise Collaboration and Communication
Trainline values teamwork, so be ready to discuss your experience working in cross-functional teams. Share examples of how you've effectively communicated complex ideas to non-technical stakeholders, showcasing your ability to collaborate and drive projects forward.
