Machine Learning Engineer

We’re looking for a Machine Learning Engineer to join our Search & Recommendations team, where we build the systems that power how customers discover products across ASOS. This is a highly impactful space – our recommendation and ranking models sit directly in the customer journey, powering experiences like "similar items", "people also viewed", and real‑time personalised journeys that adapt as customers browse. These systems run at global consumer scale, continuously learning from customer behaviour – including clicks, views, and interactions – to dynamically rank and surface the most relevant products in real time. Working in a cross‑functional team of machine learning engineers, data scientists, and product partners, you’ll help take models from experimentation into robust, production systems that directly influence customer experience and commercial outcomes. You’ll also contribute to the next generation of discovery at ASOS – from sequence‑based recommendation models to emerging areas like outfit generation and AI‑driven styling experiences.

Responsibilities

• Designing and building production‑grade machine learning systems that power personalisation and discovery.
• Developing and improving recommender systems and ranking models used across the customer journey.
• Deploying models into batch and real‑time environments, ensuring scalability, reliability, and performance.
• Collaborating with data scientists to translate experimental models into production‑ready systems.
• Monitoring, evaluating, and iterating on models using real‑world behavioural signals.
• Contributing to engineering best practices, tooling, and shared ML platform capabilities.

We’re keen to hear from Machine Learning Engineers who enjoy learning, collaboration, and building practical solutions.

Qualifications

• 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.

Benefits

• Employee discount.
• Employee sample sales.
• 25 days paid annual leave + an extra celebration day for a special moment.
• Private medical care scheme.
• Fixed annual payment in addition to your salary each year.
• Opportunity for personalised learning and in‑the‑moment experiences that enable you to thrive and excel in your role.

EEO Statement

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.