Machine Learning Engineer, Trainline

• 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 of understanding of data extraction, data manipulation and feature engineering techniques
• Are familiar with statistical methodologies
• Have good communication skills
• Experience with cloud infrastructure

Desirable

• 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
• 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 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