Machine Learning Engineer at ASOS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

22 Jul, 26

Salary

0.0

Posted On

23 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine learning, Deep learning, Recommender systems, Model deployment, Scalability, Software engineering, Version control, CI/CD, Containerisation, Monitoring, MLOps, GPU training, Distributed systems, Data science, Python, Communication

Industry

Retail Apparel and Fashion

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. Everyone needs some help showing up as their best self. We're Disability Confident Committed - 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 Machine Learning Engineer with an interest or background in machine learning and deep learning to join a collaborative, cross‑functional team. You’ll work closely with data scientists and engineers to build and improve machine learning solutions across a range of data‑rich use cases, with a current focus on recommender systems. Your work will help shape customer experiences across areas such as personalisation and pricing. This role offers the opportunity to contribute to production machine learning systems while continuing to develop your skills in model deployment, scalability, and engineering best practices. What you'll be doing Working with cross‑functional partners to design, build, and improve machine learning models for real‑world business problems, particularly recommender systems Helping to deploy and support batch and real‑time machine learning solutions Collaborating with data scientists to move models from experimentation into reliable, production‑ready systems Monitoring and improving model performance, system efficiency, and reliability Contributing to shared engineering standards, tools, and best practices within the team Keeping up to date with developments in machine learning and sharing relevant insights with the wider ML community at ASOS Qualifications About You We’re keen to hear from Machine Learning Engineers who enjoy learning, collaboration, and building practical solutions. You don’t need to meet every requirement below to apply - if the role sounds like a good fit for your experience or career direction, we’d encourage you to apply. Experience contributing to the development or deployment of machine learning solutions Familiarity with modern machine learning frameworks and model deployment workflows Experience training models using GPUs, or an interest in distributed and scalable systems Understanding of software engineering practices such as version control, CI/CD, containerisation, and monitoring, particularly within ML or MLOps contexts A collaborative working style, with clear communication and comfort working across disciplines A willingness to learn, share knowledge, and grow in a supportive, fast‑moving environment Additional Information BeneFITS’ Competitive salary and performance-based bonus scheme Generous employee discount and exclusive product access Structured personal and professional development opportunities Paid annual leave plus an additional day for special personal milestones Flexible benefits allowance and private medical care options Access to a variety of online learning resources and employee-led communities A supportive, inclusive, and dynamic workplace where innovation thrives

Responsibilities

You will collaborate with cross-functional teams to design, build, and deploy machine learning models, with a specific focus on recommender systems. Additionally, you will be responsible for monitoring model performance and contributing to shared engineering standards and best practices.