Senior Machine Learning Engineer (Recommendations)

Posted 8 Hours Ago
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London, England, GBR
Hybrid
Senior level
eCommerce • Retail
The Role
Design, build and productionise large-scale recommendation, ranking and search ML systems. Collaborate with scientists and engineers to develop, deploy and monitor batch and real-time deep learning models, improve system performance, mentor engineers, and shape technical standards and best practices across the ML organisation.
Summary Generated by Built In
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, with expertise in Machine Learning and Deep Learning 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 ItemsPeople 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 will be a senior IC who will be productionising machine learning systems that help our customers discover and shop complete outfits that resonate with both their personal style and current fashion trends. Our mission is to elevate the fashion experience and ship with high scale ML capabilities.

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.
  • 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. We recognise that skills and expertise can be developed through a range of experiences and career paths.

  • 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 

Skills Required

  • 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 models.
  • Experience 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 (batch and real-time).
  • Ability to provide technical leadership, mentor other engineers and collaborate across teams.
  • Strong collaboration and communication skills.

ASOS Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about ASOS and has not been reviewed or approved by ASOS.

  • Wellbeing & Lifestyle Benefits Feedback suggests perks like a sizable, shareable product discount, access to sample sales, free gym access, and on-site amenities are valued. Such lifestyle-oriented benefits are frequently highlighted as standout aspects of the package.
  • Leave & Time Off Breadth Feedback suggests employees benefit from substantial annual leave with bank holidays and an extra celebratory or birthday day off, with summer early finishes referenced in some contexts. This breadth of time-off options supports work-life balance.
  • Healthcare Strength Feedback suggests access to a private medical care scheme is a core part of the package. This contributes to a perception of strong healthcare support.

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The Company
HQ: London
3,200 Employees
Year Founded: 2000

What We Do

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. Whatever your role, asos will encourage you to be you, fulfilling your creative potential with our global reach. Push boundaries, and challenge expectations. We’re determined to succeed, so we’ll trust you to deliver. Help drive our journey to becoming the global fashion destination for 20-somethings At ASOS our 3,000+ employees are immersed in the creative worlds and have a truly entrepreneurial attitude. Our ASOSers are authentic, brave, creative and disciplined to the core and find ways to blend our passion for fashion with cutting edge technology. Sound up your street? Join us.

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