ML Engineer — Personalization & Recommender Systems

ASOS; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Employee discount

Employee sample sales

25 days paid annual leave + extra day

Private medical care scheme

Fixed annual payment

Personalised learning opportunities

Job summary

ASOS is seeking a Machine Learning Engineer for our Search & Recommendations team in London. You will design and build production ML systems powering personalization and product discovery across millions of customers.

You’ll collaborate with scientists and engineers to move models from experiments to robust production, deploying in batch and real-time environments while focusing on reliability, scalability and performance at scale.

Qualifications

• Experience deploying ML solutions in production environments.
• Familiarity with PyTorch, TensorFlow, XGBoost or similar frameworks.
• Experience training models on GPUs and scalable ML systems.
• Strong software engineering fundamentals: version control, CI/CD, testing, observability and containerisation.

Responsibilities

• Design, build and maintain production-grade ML systems powering personalization and discovery.
• Develop and improve recommender systems, ranking models and customer-facing ML capabilities.
• Deploy models into batch and real-time environments with reliability and scalability.
• Collaborate with Applied Scientists and Engineers to productionize models.
• Monitor, evaluate and iterate on models using real-world customer behaviour and metrics.
• Contribute to MLOps tooling and ML platform capabilities.
• Improve how ML is developed, deployed and operated across the organisation.

Skills

Production ML experience

GPU training

CI/CD and observability

Cross-functional collaboration

Curiosity and fast learning

Tools

PyTorch

TensorFlow

XGBoost

Docker

Job description

ASOS is seeking a Machine Learning Engineer for our Search & Recommendations team in London. You will design and build production ML systems powering personalization and product discovery across millions of customers.

You’ll collaborate with scientists and engineers to move models from experiments to robust production, deploying in batch and real-time environments while focusing on reliability, scalability and performance at scale.