Machine Learning Engineer
AI in this role
We’re ASOS, the online retailer for fashion lovers all around the world.
We exist to give our customers 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 judgement, and channel your creativity into a platform used by millions.
Everyone needs some help showing up as their best self. We're Disability Confident Committed - Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you.
We're looking for a Machine Learning Engineer to join our Search & Recommendations team, where we build the machine learning systems that help millions of customers discover products every day.
From personalised recommendations and product ranking to emerging AI-powered styling experiences, our work sits at the heart of the customer journey and directly influences how customers explore and shop on ASOS.
Our recommendation and ranking systems power experiences such as Similar Items, People Also Viewed, and personalised customer journeys that adapt in real time based on customer behaviour. These systems operate at significant scale, using signals from millions of interactions to surface the most relevant products and content.
As a Machine Learning Engineer, you'll work across the full machine learning lifecycle – from experimentation and model development through to deployment, monitoring and optimisation in production environments. You'll collaborate closely with Machine Learning Engineers, Applied Scientists, Software Engineers and Product partners to transform ideas into reliable, scalable systems that deliver measurable customer and commercial impact.
You'll also help shape the future of discovery at ASOS, contributing to areas such as next-generation recommendation systems, sequence-based modelling, outfit generation and AI-driven styling experiences.
What you'll be doing
- Designing, building and maintaining production-grade machine learning systems that power personalisation and product discovery
- Developing and improving recommender systems, ranking models and customer-facing machine learning capabilities
- Deploying models into batch and real-time environments, ensuring reliability, scalability and performance at scale
- Collaborating with Applied Scientists and Engineers to take models from experimentation into robust production systems
- Monitoring, evaluating and iterating on models using real-world customer behaviour and performance metrics
- Contributing to engineering best practices, MLOps tooling and shared machine learning platform capabilities
- Helping to improve how machine learning is developed, deployed and operated across the organisation
About You
We're keen to hear from Machine Learning Engineers who enjoy solving real-world problems, learning from others and building systems that deliver meaningful impact.
You don't need to meet every requirement below to apply. If this role sounds exciting and aligns with your experience or career ambitions, we'd love to hear from you.
- Experience developing, deploying or operating machine learning solutions in production environments
- Familiarity with modern machine learning frameworks and tooling such as PyTorch, TensorFlow, XGBoost or similar technologies
- Experience training models using GPUs, or an interest in distributed computing and scalable machine learning systems
- Understanding of software engineering fundamentals, including version control, CI/CD, testing, observability and containerisation
- An appreciation of MLOps practices and the challenges of deploying machine learning systems at scale
- Strong collaboration and communication skills, with experience working across engineering, science and product disciplines
- Curiosity, adaptability and a genuine enthusiasm for learning new technologies and approaches
BeneFITS’
- Employee discount (hello ASOS discount!)
- Employee sample sales
- 25 days paid annual leave + an extra celebration day for a special moment
- Private medical care scheme
- Fixed Annual Payment in addition to your salary each year, it's just an extra thank you from us
- Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role
How we rate this
Machine Learning Engineer at ASOS rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of TensorFlow that you've run into, and how did you work around them?
- What's a project where you used XGBoost hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, and XGBoost. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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