Principal Machine Learning Engineer

Qualifications

Experience 

We're interested in candidates with significant experience across a number of the following areas. We recognise that expertise can be developed through different career paths and experiences. 

• 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 with a 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. 

Technical expertise 

• Deep expertise in two or more of the following areas: ranking and relevance, recommendation systems, deep learning, large language models, information retrieval, natural language processing or content understanding. 
• Advanced hands-on experience with frameworks such as 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 retrieval-augmented generation (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. 

Leadership capabilities 

• 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. 

Additional Information

BeneFITS’ 

• Employee discount (hello ASOS discount!) 
• Employee sample sales 
• 25 days paid annual leave + an extra celebration day for a special moment 
• Discretionary bonus scheme 
• Private medical care scheme 
• Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits 
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role 

Company Description

We're ASOS, the online retailer for fashion lovers all around the world.

We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you're free to be your true self without judgement, and channel your creativity into a platform used by millions.

Everyone needs some help showing up as their best self. We're Disability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process

Job Description

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.

Technical Leadership & Engineering Excellence

• Provide technical leadership on strategic initiatives, including platform investment decisions and build-versus-buy evaluations.
• Mentor and support Senior, 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.

Stakeholder Influence

• 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 both technical and non-technical audiences.