machine learning engineer for fashion discovery

ASOS is an online fashion retailer serving customers around the world. Its Search & Discovery organisation develops recommendation systems, personalisation, deep learning and large language model technologies that help customers discover fashion.

• 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
• 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
• Provide technical leadership on strategic initiatives, including platform investment decisions and build-versus-buy evaluations
• Mentor and support Senior and Staff-level engineers, helping develop engineering capability across the organisation
• Promote engineering excellence, modern software engineering practices and responsible adoption of AI-assisted development tools
• Contribute to technical standards, architectural principles and engineering best practices across multiple teams
• Drive the development of shared machine learning capabilities, tools and frameworks used across Search & Discovery and wider engineering teams
• Represent Search & Discovery engineering in discussions with senior stakeholders, technology partners and business leaders
• Communicate technical strategy, trade-offs and outcomes clearly to technical and non-technical audiences

• Extensive experience designing, building and operating large-scale machine learning systems in production environments
• Experience owning and influencing technical architecture across complex engineering ecosystems
• A product-focused mindset and passion for applying machine learning and AI to customer and business challenges
• Extensive experience across the machine learning lifecycle, including data analysis, feature engineering, model development, evaluation, deployment, monitoring and continual improvement
• Experience building scalable, observable and highly reliable machine learning services using cloud-based technologies, distributed infrastructure and large datasets
• Deep expertise in two or more of: ranking and relevance, recommendation systems, deep learning, large language models, information retrieval, natural language processing or content understanding
• Advanced hands-on experience with PyTorch, TensorFlow or similar machine learning frameworks
• Strong programming skills in Python and/or other languages such as Java or C++
• Deep understanding of MLOps practices, including deployment, observability, monitoring and lifecycle management at scale
• Experience building production AI systems using approaches such as RAG, agent-based architectures, retrieval systems, model evaluation frameworks and ML-driven scoring approaches
• Significant experience using AI-assisted engineering tools and coding agents such as Claude Code, Codex, Cursor or similar technologies throughout the software development lifecycle
• Ability to establish and communicate a compelling technical vision and influence multiple teams without direct management responsibility
• Demonstrated experience setting architectural direction, driving engineering strategy and encouraging adoption of technical standards across teams
• Experience mentoring senior engineers and supporting wider engineering development
• Strong communication and stakeholder management skills, including engagement with senior technical and business leaders

• Employee discount
• 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 to use towards other benefits
• Personalised learning and in-the-moment development opportunities