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- Tasks: Join our team to design and deliver innovative machine learning systems that enhance train travel.
- 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 creating impactful AI solutions for millions of users.
- Qualifications: You should have experience in machine learning, Python, and a relevant degree in a quantitative field.
- Other info: We value diversity and inclusivity, fostering a culture where everyone belongs.
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 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 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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Are skilled in one of reinforcement learning, predictive modelling (classification and regression) and have a solid understanding of recommendation systems
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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 with Spark
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Have experience in productionising machine learning modelsand/or real-time systems
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Have knowledge of DevOps technologies such as Docker and Terraform, building APIs, CI/CD processes and tools, and MLOps practices and platforms like MLFlow and monitoring
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Have experience with agile delivery methodologies
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Have good communication skills
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Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline
Nice to have
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Hands-on building large-scale reinforcement learning solutions for content recommendation
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Experience with transport industry
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Experience with cloud infrastructure
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Experience with graph technology and/or algorithms
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Have experience with NLP, designing, fine-tunning and developing GenAI models and building agent AI systems
Our technology stack
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Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, TensorFlow, etc…)
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PySpark
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AWS cloud infrastructure: EMR, ECS, S3, Athena, etc.
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MLOps: Terraform, Docker, Airflow, MLFlow, Jenkins
On call statement:
Please be aware that our Machine Learning Engineers are required to be a part of the technology on-call rota. More details on how this works can be shared by the recruiter.
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 !
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Machine Learning Engineer - Recommendations & Reinforcement Learning employer: Trainline
Contact Detail:
Trainline Recruiting Team
StudySmarter Expert Advice š¤«
We think this is how you could land Machine Learning Engineer - Recommendations & Reinforcement Learning
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
Engage with the Machine Learning community at Trainline by following their social media channels and participating in discussions. This will not only keep you updated on their latest projects but also demonstrate your genuine interest in their work.
āØTip Number 3
Prepare to discuss specific machine learning projects you've worked on, especially those involving reinforcement learning or recommendation systems. Be ready to explain your thought process, challenges faced, and how you overcame them, as this will highlight your problem-solving skills.
āØTip Number 4
Network with current employees or alumni who have experience in similar roles. They can provide insights into the company culture and expectations, which can be invaluable when tailoring your approach for the interview.
We think you need these skills to ace Machine Learning Engineer - Recommendations & Reinforcement Learning
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 expertise in data extraction, manipulation, and feature engineering, as well as your proficiency in Python and machine learning libraries.
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 ability to design and deliver impactful ML systems.
Showcase Your Projects: If you have worked on relevant projects, especially those involving reinforcement learning or recommendation systems, include them in your application. Provide links to your GitHub or portfolio to showcase your work and problem-solving skills.
Highlight Soft Skills: Trainline values good communication and teamwork. Make sure to mention any experiences where you've successfully collaborated with cross-functional teams or communicated complex ideas effectively, as this will resonate with their team-oriented culture.
How to prepare for a job interview at Trainline
āØShowcase Your Technical Skills
Be prepared to discuss your experience with machine learning frameworks and libraries, especially Python and its associated tools like Scikit-learn and TensorFlow. Highlight any projects where you've implemented reinforcement learning or recommendation systems.
āØUnderstand Trainline's Mission
Familiarise yourself with Trainline's goals around sustainable travel and how machine learning plays a role in achieving these objectives. This will help you align your answers with the company's vision during the interview.
āØPrepare for Problem-Solving Questions
Expect to tackle technical challenges or case studies related to data manipulation, feature engineering, and model evaluation. Practising these types of questions can help you demonstrate your analytical skills effectively.
āØEmphasise Collaboration and Communication
Since the role involves working in cross-functional teams, be ready to discuss your experience collaborating with data scientists, software engineers, and product managers. Highlight examples where your communication skills led to successful project outcomes.
