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Salary
$91k – $187k per year (Estimated)
Location
Remote/Hybrid (London, United Kingdom)
Seniority
Senior
Employment
Full-Time
Overview
Company
Impact
Profile match
ASOS is a major British online fast-fashion and cosmetics retailer targeted primarily at young adult consumers. Founded in London in 2000, the e-commerce giant curates a selection of over 850 partner brands alongside its proprietary fashion labels, including ASOS Design and Topshop. Operating through localized digital storefronts and global distribution hubs, the company fulfills orders for millions of active customers across more than 150 countries.

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.

We’re looking for a Senior Machine Learning Engineer to join our cross-functional Personalisation team. In this role, you’ll help design, build and run machine learning systems that enable customers to discover and shop outfits that reflect their personal style as well as current fashion trends.

This is a senior individual contributor role where you’ll work closely with engineers, data scientists and product partners to productionise machine learning at scale. Our mission is to continuously improve the customer experience through thoughtful, responsible and high-impact use of machine learning.

What you’ll be doing

  • Working as part of an agile, cross-functional team to build and improve algorithms used in areas such as pricing and customer targeting
  • Collaborating with scientists and engineers to implement and deploy machine learning solutions at scale, supporting hundreds of millions of products and customers
  • Deploying and maintaining both batch and real-time machine learning models in production environments
  • Improving and evolving our codebase, tooling and platforms, and contributing to the design of new features and capabilities
  • Supporting and mentoring more junior team members, helping them develop their technical skills and confidence
  • Contributing to technical direction, helping define machine learning standards, and sharing knowledge across ASOS’s wider ML and engineering community

About you:

  • Professional experience applying machine learning in real-world, production environments, with a focus on deep learning techniques
  • Experience working with recommendation or ranking systems (or a strong interest in this space)
  • Familiarity with modern deep learning frameworks and distributed computing approaches for training large-scale models
  • Experience training models across GPUs using data and/or model parallelism, or enthusiasm to deepen your knowledge in this area
  • A solid understanding of software engineering principles, including data pipelines, CI/CD, containerisation and observability, with exposure to MLOps practices and tooling
  • Comfortable providing technical guidance, mentoring and support to a small number of less-experienced engineers
  • Enjoy collaborating across teams and contributing to shared engineering initiatives

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
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