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Junior Machine Learning Engineer

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 £5.3 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, Berlin, Madrid, and Brussels. With our focus on growth in the UK and Europe, now is the perfect time to join us on this high-speed journey. 

💻 Senior Machine Learning Engineer 📍London (Hybrid, 40% in office) 💸 £40,000 - £50,000 + Benefits 

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 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  

  • 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 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 😍 

  • Experience with transport industry and/or geographical information systems (GIS)  

  • Experience with cloud infrastructure  

  • Understanding of NLP algorithms and techniques  and/or experience with Large Language Models (fine tuning, RAG, agents)  

  • Experience with graph technology and/or algorithms 

Our technology stack 💻  

  • Python and associated ML/DS libraries (scikit-learn, NumPy, LightGBM, Pandas, LangChain/LangGraph, TensorFlow, etc...) 

  • PySpark 

  • AWS cloud infrastructure: EMR, ECS, Athena, etc.  

  • MLOps: Terraform, Docker, Airflow, MLFlow  

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, 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: 

  • 💭 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 

Interested in finding out more about what it's like to work at Trainline? Why not check us out on LinkedIn, Instagram and Glassdoor

Trainline Glassdoor Company Review
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Average salary estimate

$45000 / YEARLY (est.)
min
max
$40000K
$50000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Junior Machine Learning Engineer, Trainline

Are you ready to embark on an exciting journey with Trainline as a Junior Machine Learning Engineer? Based in vibrant London, this role is perfect for tech-savvy individuals eager to dive into the world of machine learning and AI while contributing to sustainable travel solutions. At Trainline, we’re not just about selling train tickets; we’re transforming how millions travel every day through our popular mobile app, website, and partner channels. As part of our dynamic machine learning team, you’ll help shape the future of train travel by developing advanced models that power critical features like personalized user experiences, pricing optimizations, and data-driven marketing systems. And don’t worry if you’re new to the field; we highly value growth and learning, so you’ll be surrounded by experienced engineers and data scientists cheering you on! What’s more, you’ll own the full machine learning lifecycle, from ideation to deployment, ensuring your work makes a real impact. With competitive pay, a passion for innovation, and a commitment to sustainability, there’s no better place to supercharge your career. Come join Trainline and help us build a greener future for travel!

Frequently Asked Questions (FAQs) for Junior Machine Learning Engineer Role at Trainline
What qualifications do I need to be a Junior Machine Learning Engineer at Trainline?

To be considered for the Junior Machine Learning Engineer position at Trainline, candidates should have an advanced degree in Computer Science, Mathematics, or a related field. Proficiency in Python and experience with machine learning libraries such as Pandas and Scikit-learn are essential. Familiarity with data extraction, manipulation techniques, and productionizing machine learning models will also be valuable for this role.

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What will my responsibilities be as a Junior Machine Learning Engineer at Trainline?

As a Junior Machine Learning Engineer at Trainline, you will be part of cross-functional teams to design and deliver machine learning models at scale. Your responsibilities will include exploring data, performing feature engineering, model selection, deployment, and maintenance, as well as collaborating with stakeholders to develop innovative data products that leverage Trainline’s datasets.

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What programming languages and tools should I be proficient in for the Junior Machine Learning Engineer position at Trainline?

For the Junior Machine Learning Engineer role at Trainline, candidates should be proficient in Python, especially with libraries such as Scikit-learn, NumPy, and TensorFlow. Experience with Spark and knowledge of DevOps tools like Docker and Terraform, as well as ML Ops practices, are also critical for ensuring successful project delivery.

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Is experience in the transport industry necessary for the Junior Machine Learning Engineer role at Trainline?

While not mandatory, experience in the transport industry can be a nice-to-have for the Junior Machine Learning Engineer position at Trainline. Familiarity with geographical information systems (GIS) and an understanding of NLP algorithms can also enhance your profile and contribute to our mission of making travel simpler and more sustainable.

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What perks and opportunities for growth can I expect as a Junior Machine Learning Engineer at Trainline?

As a Junior Machine Learning Engineer at Trainline, you can look forward to fantastic perks, including private healthcare, a generous work-from-abroad policy, and excellent family benefits. We prioritize career growth with clear advancement paths, transparent pay bands, and dedicated personal learning budgets, ensuring you're supported throughout your journey with us.

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Common Interview Questions for Junior Machine Learning Engineer
Can you explain the machine learning lifecycle?

The machine learning lifecycle consists of several key stages: problem definition, data collection, data preprocessing, feature engineering, model selection, training, evaluation, and deployment. Being familiar with this process will help you articulate how you plan to approach projects during your interview.

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What types of machine learning models are you familiar with?

It's important to discuss your experience with various models like classification, regression, and clustering. You could explain when to use a decision tree versus a neural network based on project needs, showcasing your knowledge and experience in the field.

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How do you ensure the quality of data used in machine learning models?

To ensure high-quality data, I focus on proper data cleaning, normalization, and validation techniques. Highlighting your understanding of data integrity and preprocessing methods will demonstrate your seriousness about maintaining data quality.

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Describe a time when you had to troubleshoot a machine learning model that was not performing as expected.

For this question, it's best to structure your answer using the STAR method, detailing the situation, the task at hand, the actions you took to address the issue, and the results of your troubleshooting. This showcases your problem-solving skills.

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What is feature engineering, and why is it important?

Feature engineering involves selecting and transforming raw data into features that better represent the underlying problem to predictive models. Emphasizing its significance will illustrate your understanding of how to improve model performance.

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Explain the concept of overfitting and how to avoid it.

Overfitting occurs when a model is too complex and learns noise instead of the underlying pattern. This leads to poor generalization to new data. Techniques like cross-validation, pruning, and regularization can be deployed to mitigate this issue, demonstrating your technical grasp.

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How do you handle imbalanced datasets?

Dealing with imbalanced datasets can often skew your model's performance. Discuss techniques such as resampling, using appropriate evaluation metrics, or employing algorithms specifically designed to handle imbalance, showing your awareness of real-world data challenges.

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What libraries and tools do you commonly use for machine learning tasks?

Familiarity with tools like Scikit-learn, TensorFlow, and Keras for model building, Pandas for data manipulation, and Matplotlib or Seaborn for visualization indicates your readiness to work with common technologies in the industry.

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How do you stay updated with the latest advancements in machine learning?

Discuss your methods for staying informed, whether it’s through online courses, reading research papers, or following key influencers. A growth mindset is crucial for roles at the cutting edge of technology like the Junior Machine Learning Engineer position at Trainline.

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Why do you want to work as a Junior Machine Learning Engineer at Trainline?

In answering this question, emphasize your passion for machine learning and how you wish to contribute to Trainline's sustainable travel mission. Tailoring your response to align your professional goals with the company's vision will demonstrate your genuine interest in the role.

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We are Europe’s leading independent rail and coach platform. Our aim is to bring together all rail, coach and other travel services into one simple experience so people can get the best prices and smart, real time travel info on the go.

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Full-time, hybrid
DATE POSTED
February 28, 2025

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