Senior Machine Learning Engineer (RecommendationsSearch)

Were looking for a Senior Machine Learning Engineer to join our Search and Recommendations team where were building the next generation of AI-powered fashion experiences at ASOS.

Our mission is to help millions of customers discover complete outfits that reflect their personal style preferences and the latest fashion trends. Sitting within ASOSs Search & Discovery organisation the team combines recommendation systems personalisation deep learning and emerging AI technologies to create new ways for customers to discover fashion beyond traditional ecommerce experiences.

Youll work on large-scale machine learning systems powering personalised outfit recommendations style discovery and intelligent product experiences across the customer journey. From recommendation and retrieval systems to deep learning and generative AI applications youll help bring innovative ideas into production and deliver experiences used by millions of customers.

Working alongside Machine Learning Scientists Software Engineers and Product Managers youll play a key role in designing building and operating production ML systems at scale. Youll tackle challenging problems across recommendation systems personalisation deep learning and AI-powered outfit generation helping shape the future of machine learning at ASOS.

What youll be doing:

• Design build and operate production machine learning systems that power outfit discovery and personalised fashion experiences.
• Partner with Machine Learning Scientists to deploy deep learning models and deliver meaningful customer and business outcomes.
• Deploy and optimise batch and real-time machine learning models serving millions of customers.
• Contribute to systems that power recommendations personalisation and AI-driven fashion discovery experiences across ASOS.
• Improve system performance reliability observability and scalability across the machine learning lifecycle.
• Contribute to technical design decisions architecture discussions and engineering best practices.
• Mentor and support other engineers through coaching collaboration and knowledge sharing.
• Help strengthen technical practices across the team and the wider machine learning community at ASOS.
• Contribute to the development of shared machine learning capabilities tools and best practices used across multiple teams.

Qualifications :

About You

Were interested in candidates who bring experience in several of the following areas. We recognise that skills and expertise can be developed through a variety of experiences and career paths.

• Experience designing building and deploying machine learning systems in production environments.
• Strong understanding of machine learning engineering principles and modern software engineering practices.
• Hands-on experience with deep learning frameworks such as PyTorch TensorFlow or similar.
• Experience training and optimising models using large datasets and distributed compute infrastructure.
• Experience working with recommendation systems ranking retrieval personalisation or related machine learning domains.
• Knowledge of MLOps practices including model deployment monitoring and lifecycle management.
• Experience building reliable observable and scalable services in cloud environments.
• Comfortable providing technical leadership and mentoring other engineers.
• Strong collaboration and communication skills with experience working in cross-functional product teams.
• Curiosity about emerging AI technologies and the practical application of LLMs and generative AI in customer-facing products.

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 

Remote Work :

No

Employment Type :

Full-time