24 hours ago
London, United KingdomSenior
Responsibilities
- Design, build, and operate machine learning systems for customer engagement, marketing effectiveness, pricing, and commercial decision-making.
- Own the end-to-end engineering lifecycle of machine learning products, including data ingestion, feature engineering, deployment, monitoring, and optimisation.
- Productionise advanced machine learning solutions and ensure reliable operation at ASOS scale.
- Partner with Applied Scientists to translate research and experimentation into scalable production systems.
- Contribute to the MLOps platform, engineering practices, operational excellence, and platform capabilities.
- Build reusable tooling, frameworks, and infrastructure that accelerate machine learning delivery.
- Influence technical direction and architectural decisions across machine learning products and platforms.
- Mentor colleagues and support high standards of engineering quality, reliability, and scalability.
Requirements
- Experience building, deploying, and operating machine learning systems in production environments.
- Strong software engineering fundamentals and expertise in Python.
- Experience building scalable batch and real-time machine learning pipelines in cloud environments.
- Strong understanding of MLOps principles, including model deployment, monitoring, observability, and operational excellence.
- Experience with large-scale data processing technologies such as Spark.
- Strong understanding of machine learning frameworks such as PyTorch, TensorFlow, XGBoost, or similar technologies.
- Experience designing reliable APIs, services, and platforms that support machine-learning-powered products.
- Ability to work through ambiguity and lead complex technical initiatives.
- Experience in customer intelligence, marketing optimisation, pricing, forecasting, or personalisation.
- Experience building feature platforms, ML platforms, or shared machine learning infrastructure.
- Exposure to experimentation frameworks, causal inference, or measurement platforms.
- Experience mentoring engineers and influencing technical direction beyond the immediate team.
- A track record of delivering machine learning solutions with measurable customer or commercial outcomes.
Benefits
- Employee discount and access to employee sample sales.
- 25 days of paid annual leave plus an extra celebration day.
- Discretionary bonus scheme.
- Private medical care scheme.
- Flexible benefits allowance that can be taken as extra cash or used for other benefits.
- Personalised learning and in-the-moment development opportunities.
Tech Stack
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.