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- Tasks: Join our team to design and deliver innovative machine learning systems that enhance travel experiences.
- Company: Trainline is Europe's leading rail app, dedicated to sustainable travel and customer satisfaction.
- Benefits: Enjoy perks like private healthcare, work-from-abroad options, and generous learning budgets.
- Why this job: Be part of a mission-driven team focused on making travel eco-friendly and affordable for millions.
- Qualifications: Advanced degree in a quantitative field and proficiency in Python and machine learning techniques required.
- Other info: We value diversity and foster an inclusive workplace where every voice matters.
The predicted salary is between 42000 - 84000 Ā£ 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 Europeās number 1 downloaded rail app, with over 125 million monthly visits and Ā£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, eco-friendly 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:
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AI agents improving customer support and changing how we travel
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Advanced search and recommendations capabilities across our mobile and web applications
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Pricing and routing optimisations to find the best fares for customers
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Personalised user experiences enhanced by generative AI
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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 Trainlineās 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 Trainlineās 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…
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Work in cross-functional teams combining data scientists, software, data and machine learning engineers, and product managers
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Design and deliver NLP based machine learning systems at scale that drive measurable impact for our business
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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
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Partner with stakeholders to propose innovative data products that leverage Trainlineās extensive datasets and state of the art algorithms
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Create the tools, frameworks and libraries that enables the acceleration of our ML products delivery and improve our workflows
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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…
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Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline
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Understanding of NLP algorithms and techniquesand/or experience with Large Language Models (fine tuning, RAG, agents)
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Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.)
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Have experience productionising machine learning models
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Are an expert in one of predictive modeling, classification, regression, optimisation or recommendation systems
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Have experience with Spark
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Have knowledge of DevOps technologies such as Docker and Terraform and ML Ops practices and platforms like ML Flow
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Have experience with agile delivery methodologies and CI/CD processes and tools
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Have a broad of understanding of data extraction, data manipulation and feature engineering techniques
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Are familiar with statistical methodologies.
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Have good communication skills
Nice to have
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Experience with LangGraph or LangChain
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Experience with transport industry and/or geographical information systems (GIS)
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Experience with cloud infrastructure
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Experience with graph technology and/or algorithms
Our technology stack
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Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc…)
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PySpark
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AWS cloud infrastructure: EMR, ECS, Athena, etc.
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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, 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!
Our values represent the things that matter most to us and what we live and breathe everyday, in everything we do:
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Think Big – We\’re building the future of rail
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ļø Own It – We focus on every customer, partner and journey
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Travel Together – We\’re one team
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ļø 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 !
#J-18808-Ljbffr
Machine Learning Engineer - Agentic AI employer: Trainline
Contact Detail:
Trainline Recruiting Team
StudySmarter Expert Advice š¤«
We think this is how you could land Machine Learning Engineer - Agentic AI
āØTip Number 1
Familiarise yourself with Trainline's mission and values. Understanding their commitment to sustainable travel and customer-centric solutions will help you align your responses during interviews, showcasing how your skills can contribute to their goals.
āØTip Number 2
Network with current employees or alumni who work at Trainline. Engaging with them on platforms like LinkedIn can provide insights into the company culture and the specific challenges they face, which you can address in your discussions.
āØTip Number 3
Stay updated on the latest trends in machine learning and AI, particularly in the context of the transport industry. Being able to discuss recent advancements or case studies relevant to Trainline's operations can set you apart as a knowledgeable candidate.
āØTip Number 4
Prepare to demonstrate your technical skills through practical examples. Be ready to discuss past projects where you've successfully implemented machine learning models, especially those involving NLP or large datasets, as this aligns closely with the role.
We think you need these skills to ace Machine Learning Engineer - Agentic AI
Some tips for your application š«”
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.
Craft a Compelling Cover Letter: Write a cover letter that showcases 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.
Showcase Your Projects: Include links to any relevant projects or GitHub repositories that showcase your work with machine learning models, especially those related to NLP or predictive modelling. This will give the hiring team insight into your practical skills.
Prepare for Technical Questions: Be ready to discuss your understanding of machine learning concepts, algorithms, and tools during the interview process. Brush up on topics like feature engineering, model tuning, and deployment practices to impress the interviewers.
How to prepare for a job interview at Trainline
āØShowcase Your Technical Skills
Be prepared to discuss your experience with Python and machine learning libraries like Scikit-learn and TensorFlow. Highlight specific projects where you've implemented NLP algorithms or productionised machine learning models, as this will demonstrate your technical expertise.
āØUnderstand Trainline's Mission
Familiarise yourself with Trainline's commitment to sustainable travel and how machine learning plays a role in achieving this. Be ready to discuss how your skills can contribute to their goals, particularly in enhancing customer experiences and optimising travel solutions.
āØPrepare for Problem-Solving Questions
Expect to tackle complex challenges during the interview. Practice explaining your thought process when approaching data exploration, feature engineering, and model selection. This will showcase your analytical skills and ability to work through real-world problems.
āØEmphasise Collaboration and Communication
Trainline values teamwork, so be sure to highlight your experience working in cross-functional teams. Discuss how youāve effectively communicated technical concepts to non-technical stakeholders, as this is crucial for success in a collaborative environment.
