5 hours ago
London, United KingdomStaff+
Responsibilities
- Own end-to-end technical architecture for large-scale batch and real-time machine learning systems powering personalized fashion discovery, recommendations, search, and AI Stylist experiences.
- Set technical direction and engineering best practices across recommendation systems, retrieval, personalization, search relevance, deep learning, and generative AI applications.
- Translate machine learning research and experimentation into scalable, reliable, observable, production-grade customer-facing systems.
- Drive cross-team architectural decisions, platform investment choices, build-versus-buy evaluations, and shared ML capabilities across engineering teams.
- Identify and resolve architectural, scalability, reliability, and performance challenges across the ML lifecycle.
- Establish technical standards and strategy while influencing multiple teams and communicating trade-offs to technical and non-technical stakeholders.
- Mentor Senior and Staff-level engineers and support engineering development across the organization.
Requirements
- Significant or extensive experience designing, building, and operating large-scale machine learning systems in production.
- Experience owning and influencing technical architecture across complex engineering ecosystems.
- Experience across the ML lifecycle, including data analysis, feature engineering, model development, evaluation, deployment, monitoring, and continual improvement.
- Experience building scalable, observable, 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 machine learning frameworks.
- Strong programming skills in Python and/or languages such as Java or C++.
- Deep understanding of MLOps, 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 or coding agents such as Claude Code, Codex, or Cursor.
- Demonstrated ability to establish technical vision, influence multiple teams without direct management responsibility, drive engineering strategy, and encourage technical standards adoption.
- Experience mentoring senior engineers and communicating effectively with senior technical and business leaders.
Benefits
- Employee discount and access to 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 can be taken as extra cash or used toward other benefits.
- Personalized learning and in-the-moment development experiences.
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.