Senior Machine Learning Engineer - ML Infrastructure

Job Description

As a Senior Machine Learning Engineer, you’ll focus on designing and implementing reusable ML templates, deployment patterns, and MLOps tooling that support scalable, reliable, and secure ML solutions across the organisation.

You’ll collaborate closely with ML Engineers and Scientists embedded in product teams such as Forecasting, Recommendations, Marketing, Customer, and Pricing helping them accelerate delivery and improve the quality of ML systems by providing a robust and standardised ML development experience.

What you’ll be doing:

Designing and developing shared ML engineering templates, tooling, and infrastructure to support ML teams across ASOS. Driving standardisation and reusability of ML workflows, enabling consistency across diverse product domains. Enabling teams to productionise ML models efficiently by providing best practices, templates, and technical support. Implementing and promoting ML Ops principles — including CI/CD for ML, model registries, monitoring, testing, and feature management. Collaborating with ML teams to understand pain points and evolve the platform accordingly. Partnering with Data Engineering, Platform Engineering, and Security teams to ensure scalable and cost-efficient ML infrastructure.

We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That’s why our approach to working together includes spending at least 2 days a week in the office. It’s a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed.

Qualifications

About You

Professional experience as a Machine Learning Engineer, ideally with exposure to platform or infrastructure-focused work. Solid understanding of the end-to-end ML lifecycle, from experimentation through deployment and monitoring. Proficiency in Python and familiarity with ML libraries like scikit-learn, XGBoost, PyTorch or TensorFlow. Experience with ML Ops tools and practices such as MLflow, model registries, containerisation (Docker/Kubernetes), and CI/CD pipelines. Comfortable working with cloud platforms (preferably Azure) and distributed computing environments (, Spark, Databricks). Passionate about improving developer experience through automation, standardisation, and tooling.

Additional Information

BeneFITS’ 

Employee discount (hello ASOS discount!)  ASOS Develops (personal development opportunities across the business)  Employee sample sales  Access to a huge range of LinkedIn learning materials  25 days paid annual leave + an extra celebration day for a special moment  Discretionary bonus scheme  Private medical care scheme  Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits