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