Senior Machine Learning Engineer (Personalisation)

Overview

In this role you will design, build and operate production machine learning systems at scale to power ASOS’s search, ranking and recommendation experiences. You will collaborate with cross-functional teams to deliver measurable customer and commercial impact, shaping real-time and batch models that surface relevant products and personalise journeys for millions of customers. The role blends software engineering with machine learning to advance the organisation’s ML capabilities and standards. This is an opportunity to tackle challenging problems in recommendation, search and personalisation while contributing to the future of ML at ASOS.

Pay / Benefits

• employee discount
• employee sample sales
• 25 days paid annual leave + extra celebration day
• discretionary bonus scheme
• private medical care scheme
• flexible benefits allowance

Responsibilities

• Design, build and improve ML systems powering search, ranking and recommendation experiences.
• Collaborate with Applied Scientists and engineers to deploy ML solutions delivering customer and business value.
• Build, deploy and maintain batch and real-time models in production.
• Contribute to capabilities for recommendation, ranking and personalisation supporting millions of interactions daily.
• Continuously enhance systems, codebase and engineering practices; contribute ideas for new features.
• Mentor and support other engineers through coaching and knowledge sharing.
• Contribute to the team’s technical direction and ML standards across the wider ML community.

Key requirements

• Experience applying ML and deep learning techniques in production.
• Experience with deep learning frameworks and distributed computing to build and deploy large-scale models.
• Experience with distributed training infrastructure, GPU-based training and parallelisation.
• Strong understanding of software engineering principles, lifecycles and MLOps practices.
• Experience developing reliable, scalable ML systems in production.
• Ability to provide technical leadership, mentoring and support to engineers.
• Strong collaboration and communication across engineering, science and product teams.

• Collaboration
• Communication
• Mentoring
• Production ML deployment
• Deep learning frameworks
• Distributed computing for ML

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