ASOS

Senior Machine Learning Engineer

ASOS · London, England, United Kingdom

Retail Apparel and Fashion · 1,001-5,000 employees

5 h ago
Senior (5-10 yrs) Full-time United Kingdom
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About the role

You will design, build, and operate production-scale machine learning systems to power personalized fashion discovery and outfit recommendations. You will also collaborate with cross-functional teams to deploy deep learning models and optimize system performance across the machine learning lifecycle.

What they look for

Machine learning Deep learning PyTorch TensorFlow Recommendation systems MLOps Cloud computing Software engineering Generative AI LLMs Distributed computing System architecture Mentoring Data modeling Production deployment

Requirements

Candidates should have experience building and deploying machine learning systems in production environments using deep learning frameworks like PyTorch or TensorFlow. Strong knowledge of MLOps, distributed compute infrastructure, and software engineering best practices is required.

Benefits

Employee discount Employee sample sales 25 days paid annual leave Celebration day Discretionary bonus scheme Private medical care scheme Flexible benefits allowance Personalised learning

Full description

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.

But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter and we placed 8th in the Inclusive Top 50 Companies Employer list.

Everyone needs some help showing up as their best self. Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you.

Job Description

We're looking for a Senior Machine Learning Engineer to join our Outfits Discovery team, where we're 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 ASOS's 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.

You'll 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, you'll help bring innovative ideas into production and deliver experiences used by millions of customers.

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

What you'll 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

We're 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