1 day ago
London, United KingdomStaff+
Responsibilities
- Own the end-to-end architecture and long-term technical direction for large-scale machine learning systems supporting personalized product and outfit recommendations, ranking, retrieval, search relevance, and AI Stylist experiences.
- Lead the design and evolution of scalable batch and real-time ML systems serving millions of customers.
- Translate research and experimentation into robust, production-grade customer-facing ML products.
- Drive cross-team architectural decisions, platform investment choices, build-versus-buy evaluations, and adoption of technical standards.
- Identify and resolve scalability, reliability, observability, and performance challenges throughout the ML lifecycle.
- Develop shared machine learning capabilities, tools, and frameworks for Search & Discovery and wider engineering teams.
- Mentor Senior and Staff engineers and influence technical strategy across multiple teams without direct management responsibility.
- Represent Search & Discovery engineering with senior stakeholders, technology partners, and business leaders.
Requirements
- Extensive experience designing, building, and operating large-scale machine learning systems in production environments.
- Experience owning and influencing technical architecture across complex engineering ecosystems.
- Extensive experience across the ML lifecycle, including data analysis, feature engineering, model development, evaluation, deployment, monitoring, and continual improvement.
- Experience building scalable, observable, and highly reliable ML services using cloud technologies, distributed infrastructure, and large datasets.
- Deep expertise in at least two areas including ranking and relevance, recommendation systems, deep learning, large language models, information retrieval, natural language processing, or content understanding.
- Advanced hands-on experience with PyTorch, TensorFlow, or similar ML frameworks.
- Strong programming skills in Python and/or languages such as Java or C++.
- Deep understanding of MLOps practices, including deployment, observability, monitoring, and lifecycle management at scale.
- Experience building production AI systems using retrieval-augmented generation, agent-based architectures, retrieval systems, model evaluation frameworks, and ML-driven scoring approaches.
- Significant experience using AI-assisted engineering tools and coding agents such as Claude Code, Codex, Cursor, or similar technologies.
- Ability to establish and communicate technical vision, influence multiple teams, drive engineering strategy, and encourage adoption of technical standards.
- Experience mentoring senior engineers and communicating effectively with senior technical and business leaders.
Benefits
- Employee discount and employee sample sales.
- 25 days of paid annual leave plus an additional celebration day.
- Discretionary bonus scheme.
- Private medical care scheme.
- Flexible benefits allowance that may be taken as extra cash or used toward other benefits.
- Personalized learning and in-the-moment development opportunities.
Categories
About ASOS
ASOS is a global online fashion and beauty retailer for 20‑somethings, selling own‑brand and third‑party labels via its website and mobile apps. The company earns revenue from direct‑to‑consumer e‑commerce, shipping to customers worldwide and offering marketplace services for brands. Founded in 2000 and headquartered in London, ASOS is a public company listed on the London Stock Exchange and runs in‑house content and fulfillment operations at scale.