Principal Machine Learning Engineer

We're looking for a Principal Machine Learning Engineer to join our Search & Discovery team and help define the technical direction of AI-powered fashion discovery at ASOS.

Our mission is to help millions of customers discover outfits that reflect their personal style, preferences and current fashion trends. As part of the Search & Discovery organisation, we bring together recommendation systems, personalisation, deep learning and large language model (LLM) technologies to create new ways for customers to explore fashion beyond traditional ecommerce experiences.

As a Principal Machine Learning Engineer, you'll shape the technical architecture behind large-scale machine learning systems spanning personalized product & outfit recommendations, conversational agent experiences like AI Stylist, search relevance, style discovery and intelligent product experiences across the customer journey.

You'll work closely with Machine Learning Scientists, Software Engineers, Engineering Leaders and Product Managers, providing technical leadership across Search & Discovery while remaining hands-on with architecture, system design and engineering decisions.

This is a highly influential role where you'll help shape the future of machine learning engineering at ASOS while mentoring others and driving engineering excellence across the organisation.

Technical Architecture & System Design

• Own the end-to-end technical architecture for machine learning systems powering personalised fashion experiences, including outfit discovery, homepage ranking and AI Stylist experiences. • Lead the design and evolution of large-scale batch and real-time machine learning systems serving millions of customers. • Drive cross-team architectural decisions to ensure consistency, scalability, reliability and maintainability. • Establish long-term technical direction for recommendation, retrieval and AI-powered discovery platforms.

ML Product Engineering & Production Delivery

• Set technical direction and best practices across recommendation systems, retrieval, personalisation, search relevance, deep learning and generative AI applications. • Partner with Machine Learning Scientists and Engineering Leaders to translate research and experimentation into robust production systems. • Identify and resolve architectural, scalability, reliability and performance challenges throughout the machine learning lifecycle. • Support the delivery of production-grade customer-facing ML products that generate measurable business and customer value.