Lead ML Architect for Fashion Discovery & Personalization

ASOS.com Ltd; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Employee discount (hello ASOS discount

Employee sample sales

25 days paid annual leave + an extra 1

Discretionary bonus scheme

Private medical care scheme

Flexible benefits allowance - choose/€

Personalised learning opportunities

Job summary

ASOS.com Ltd in London is seeking a Principal Machine Learning Engineer to define the technical direction of AI-powered fashion discovery. You will shape end-to-end ML architecture for personalised recommendations, AI Stylist experiences and search relevance to delight millions of customers.

You'll mentor senior engineers, drive platform choices and align ML delivery with business goals, applying retrieval, reinforcement and LLM techniques at scale while maintaining production quality and

Qualifications

• Experience designing and operating large-scale ML systems in production.
• Ability to own architectural direction across complex ML ecosystems.
• Product-focused mindset applying ML/AI to customer and business challenges.
• Experience with end-to-end ML lifecycle including deployment and monitoring.

Responsibilities

• Own end-to-end technical architecture for ML systems powering personalised experiences.
• Lead design of large-scale batch and real-time ML services serving millions.
• Set technical direction across recommender, retrieval, NLP and AI applications.
• Mentor senior engineers and promote engineering excellence across teams.

Skills

Machine Learning Systems

Leadership without mgmt

Communication

Tools

PyTorch

TensorFlow

Python

Java

C++

ML Ops

Job description

ASOS.com Ltd in London is seeking a Principal Machine Learning Engineer to define the technical direction of AI-powered fashion discovery. You will shape end-to-end ML architecture for personalised recommendations, AI Stylist experiences and search relevance to delight millions of customers.

You'll mentor senior engineers, drive platform choices and align ML delivery with business goals, applying retrieval, reinforcement and LLM techniques at scale while maintaining production quality and