Principal Machine Learning Engineer

Overview

As a Principal ML Engineer in ASOS’s Search & Discovery, you will shape the technical direction for AI-powered fashion discovery at scale. You’ll own end-to-end ML architecture spanning recommendations, search relevance, and conversational AI experiences like AI Stylist. You will mentor engineers and influence engineering excellence while collaborating with scientists, product managers and leaders. This role combines hands-on design with strategic direction to deliver impactful, customer-centered ML products. You will join a mission to help customers discover outfits aligned with personal style and trends.

Pay / Benefits

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

Responsibilities

• Own end-to-end technical architecture for ML systems powering personalised fashion experiences (outfit discovery, homepage ranking, AI Stylist).
• Design and evolve large-scale batch and real-time ML systems serving millions of customers.
• Drive cross-team architectural decisions for scalability, reliability and maintainability.
• Set technical direction across recommendation, retrieval, personalization, search relevance, deep learning and generative AI applications.
• Translate research into production systems and address architecture, scalability and performance challenges.
• Provide technical leadership, mentor senior engineers, and promote engineering best practices.
• Influence platform investments and build-versus-buy decisions; establish technical standards across teams.
• Communicate strategy, trade-offs and outcomes to technical and non-technical stakeholders.

Key requirements

• Extensive experience designing, building and operating large-scale ML systems in production.
• Experience influencing technical architecture across complex ecosystems.
• Product-focused mindset applying ML/AI to customer and business goals.
• Experience across ML lifecycle: data analysis, feature engineering, model development, evaluation, deployment, monitoring and improvement.
• Experience building scalable, observable ML services using cloud, distributed infra and large datasets.
• Deep expertise in ranking/relevance, recommendations, deep learning, LLMs, information retrieval, NLP or content understanding.
• Hands-on with PyTorch, TensorFlow or comparable frameworks; strong Python and/or Java/C++ skills.
• Strong MLOps understanding including deployment, observability, monitoring and lifecycle management at scale.
• Experience with AI-assisted engineering tools and coding agents (e.g., Claude Code, Codex, Cursor).
• Leadership capabilities: communicate vision, influence teams, mentor engineers, and manage stakeholder relations.

• Effective communication with senior technical and business leaders
• Mentorship and coaching of engineers
• Strategic thinking and consensus-building across teams
• Large-scale ML systems
• Recommendation systems
• Information retrieval

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