Senior Machine Learning Engineer (Personalisation)
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.
But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter and we placed 8th in the Inclusive Top 50 Companies Employer list.
Everyone needs some help showing up as their best self. 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 Senior Machine Learning Engineer to join our Search & Recommendations team, where we're building the machine learning systems that help millions of customers discover products every day.
Sitting within ASOS's Search & Recommenders area, the team is responsible for the recommendation, ranking and personalisation systems that sit at the heart of the customer journey. From surfacing the most relevant products and outfits to powering personalised shopping experiences, our work directly influences how customers discover and shop fashion on ASOS.
You'll work on large-scale machine learning systems that power experiences such as Similar Items, People Also Viewed and personalised customer journeys that adapt in real time. Leveraging signals from millions of customer interactions, we use recommendation systems, ranking models, deep learning and emerging AI technologies to connect customers with the products they're most likely to love.
As a Senior Machine Learning Engineer, you'll play a key role in designing, building and operating production machine learning systems at scale. Working alongside Applied Scientists, Machine Learning Engineers, Software Engineers and Product Managers, you'll help turn innovative ideas into reliable, high-performing systems that deliver measurable customer and commercial impact.
This is an opportunity to tackle challenging problems across recommendation systems, search, ranking, personalisation and deep learning, while helping shape the future of machine learning at ASOS.
What you'll be doing:
- Work as part of a cross-functional team designing, building and improving machine learning systems that power search, ranking and recommendation experiences.
- Collaborate closely with Applied Scientists and engineers to develop and deploy machine learning solutions that deliver measurable customer and commercial value.
- Build, deploy and maintain batch and real-time machine learning models in production environments.
- Contribute to recommendation, ranking and personalisation capabilities that support millions of customer interactions each day.
- Continuously improve our systems, codebase and engineering practices while contributing ideas for new features and capabilities.
- Support and mentor other engineers through coaching, knowledge sharing and technical collaboration.
- Contribute to the team's technical direction and help evolve machine learning standards, best practices and ways of working across the wider ML community.
About You
We're interested in candidates who bring experience in several of the following areas. You'll likely be someone who enjoys combining strong software engineering fundamentals with machine learning expertise and is excited by the challenge of building reliable, scalable systems that deliver real-world impact.
You may come from a recommendation systems, search, ranking, personalisation, deep learning or broader machine learning background. Most importantly, you'll enjoy solving complex technical problems, collaborating across disciplines and helping bring machine learning products from experimentation into production.
- Experience applying machine learning and deep learning techniques in production environments.
- Experience using deep learning frameworks and distributed computing technologies to build and deploy large-scale machine learning models.
- Experience working with distributed training infrastructure, GPU-based training environments and parallelisation approaches.
- Strong understanding of software engineering principles, development lifecycles and MLOps practices.
- Experience developing reliable, scalable machine learning systems in production.
- Comfortable providing technical leadership, mentoring and support to other engineers.
- Strong collaboration and communication skills, with the ability to work effectively across engineering, science and product teams.
BeneFITS’
- 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
How we rate this
Senior Machine Learning Engineer (Personalisation) at ASOS rates 96 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?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: ML Ops. 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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