machine learning engineer for ecommerce

ASOS is a fashion and technology company whose platform is used by millions. Machine learning supports how customers discover products, engage with the brand and shop.

• Design, build and operate machine learning systems for customer engagement, marketing effectiveness, pricing and commercial decision-making
• Own the end-to-end engineering lifecycle of machine learning products, including data ingestion, feature engineering, deployment, monitoring and optimisation
• Productionise advanced machine learning solutions and ensure they operate reliably at ASOS scale
• Partner with Applied Scientists to turn research and experimentation into scalable production systems
• Contribute to engineering best practices, operational excellence and capabilities for the MLOps platform
• Build reusable tooling, frameworks and infrastructure to accelerate machine learning delivery and reduce operational overhead
• Influence technical direction and architectural decisions across machine learning products and platforms
• Mentor colleagues and support high standards of engineering quality, reliability and scalability

• Experience building, deploying and operating machine learning systems in production environments
• Strong software engineering fundamentals, including expertise in Python and modern engineering practices
• Experience building scalable batch and real-time machine learning pipelines in cloud environments
• Strong understanding of MLOps principles, including model deployment, monitoring, CI/CD, observability and operational excellence
• Experience with large-scale data processing technologies such as Spark
• Strong understanding of machine learning frameworks such as PyTorch, TensorFlow, XGBoost or similar technologies
• Experience designing reliable APIs, services and platforms that support machine-learning-powered products
• Ability to work through ambiguity and lead complex technical initiatives
• Experience in customer intelligence, marketing optimisation, pricing, forecasting or personalisation
• Experience building feature platforms, ML platforms or shared machine learning infrastructure
• Exposure to experimentation frameworks, causal inference or measurement platforms
• Experience mentoring engineers and influencing technical direction beyond the immediate team
• Track record of delivering machine learning solutions with measurable customer or commercial outcomes

• Employee discount and employee sample sales
• 25 Days of paid annual leave plus an extra celebration day
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance, available as extra cash or for other benefits
• Personalised learning and in-the-moment experiences