Junior ML Engineer — Hybrid, Impact at Scale

Trainline; Greater London; Hybride; GBP 40 000 - 50 000; Plein temps

Avantages offerts par ce poste

Private healthcare

Dental insurance

Work from abroad policy

Share purchase plan

EV scheme

Résumé du poste

Trainline is hiring a Junior Machine Learning Engineer to join a cross-functional AI/ML platform in a hybrid UK role. You will work with data scientists, software and product teams to tackle challenges by combining Trainline’s data with modern algorithms.

You will design scalable models, participate in end-to-end delivery, and grow with a team focused on innovative ML solutions and a culture of learning. Compensation ranges £40K–£50K with private healthcare and flexible policies.

Qualifications

• Advanced degree in Computer Science or a similar quantitative field.
• Proficiency with Python and open-source data libraries (Pandas, NumPy, Scikit-learn).
• Experience productionising ML models and working with Spark/PySpark.

Responsabilités

• Design and deliver scalable machine learning models that drive measurable impact.
• Own end-to-end ML delivery lifecycle: data exploration, feature engineering, model selection, evaluation, deployment.
• Collaborate with data scientists, software engineers and product managers in cross-functional teams.

Connaissances

Python

Pandas

NumPy

Scikit-learn

PySpark

Spark

Docker

Terraform

MLFlow

CI/CD

Agile delivery

AWS

Formation

Advanced degree in Computer Science or similar quantitative discipline

Outils

Docker

Terraform

MLFlow

Description du poste

Trainline is hiring a Junior Machine Learning Engineer to join a cross-functional AI/ML platform in a hybrid UK role. You will work with data scientists, software and product teams to tackle challenges by combining Trainline’s data with modern algorithms.

You will design scalable models, participate in end-to-end delivery, and grow with a team focused on innovative ML solutions and a culture of learning. Compensation ranges £40K–£50K with private healthcare and flexible policies.