About
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.
Responsibilities
•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
Requirements
•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
Benefits
•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
