ASOS
Senior Machine Learning Engineer (Recommendations)

How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Company Description
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.
Job Description
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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt 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.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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 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.
Qualifications
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.


Get help with your application
Your very own career expert that helps elevate your application to the next level.
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.
Additional Information
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
“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
Skills
Location