ASOS.com
Senior Machine Learning Engineer
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
We're ASOS
We blend our flair for fashion with our love of cutting-edge technology, but more importantly, we're interested in how we can bring the best out of you.
We exist to give people the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you're free to be your true self without judgment, and channel your creativity into a platform used by millions.
Job Description
At ASOS, machine learning is a core part of how millions of customers discover products, engage with our brand, and shop every day.
We're looking for a Senior Machine Learning Engineer to join our Customer & Martech team. In this role, you'll help build and scale machine learning products that support customer growth, marketing effectiveness, pricing, and personalisation.
You will work with some of ASOS's richest datasets, including customer behaviour, transactions, marketing interactions, and product data, turning these into production-grade machine learning systems that deliver measurable value for customers and the business.
Working alongside Applied Scientists, Data Engineers, and Machine Learning Engineers, you'll contribute across the full lifecycle of machine learning products, from ideation and experimentation through to deployment, monitoring, and optimisation.
Whether improving customer retention, optimising marketing investment, supporting intelligent pricing decisions, or helping build the next generation of customer experiences, you'll work on complex challenges at significant scale.
What you'll be doing:
- Design, build, and operate machine learning systems that support 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 they operate reliably at ASOS scale.
- Partner closely with Applied Scientists to translate research and experimentation into scalable production systems.
- Help shape the future of our MLOps platform by contributing to engineering best practices, operational excellence, and platform capabilities.
- Build reusable tooling, frameworks, and infrastructure that accelerate machine learning delivery and reduce operational overhead.
- Influence technical direction and architectural decisions across machine learning products and platforms.
- Mentor colleagues and support high standards of engineering quality, reliability, and scalability.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
This is an opportunity to work on machine learning products used by millions of customers, leveraging rich datasets across customer behaviour, marketing, pricing, and ecommerce. You'll collaborate with Applied Scientists, Machine Learning Engineers, and Data Engineers to solve complex challenges at the intersection of machine learning, software engineering, and large-scale data systems, while seeing the direct impact of your work on customer experience and commercial outcomes. You'll also help shape the future of ASOS's machine learning platform and engineering standards in an environment where machine learning is a core business capability.
Qualifications
We recognise that people may not meet every requirement listed above. If your experience is relevant to the role and you believe you could contribute to the team, we encourage you to apply.
- Experience building, deploying, and operating machine learning systems in production environments.
- Strong software engineering fundamentals, including expertise in Python and modern engineering practices.
- Experience building scalable batch and real-time machine learning pipelines in cloud environments.
- Strong understanding of MLOps principles, including model deployment, monitoring, CI/CD, observability, and operational excellence.
- Experience working 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 your immediate team.
- A track record of delivering machine learning solutions that generated measurable customer or commercial outcomes.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Additional Information
What's in it for you?
- Employee discount (hello ASOS discount!)
- Employee sample sales
- 25 days paid annual leave + an extra celebration day for a special moment
- Discretionary bonus scheme
- Private medical care scheme
- Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
- Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Location