At a Glance
- Tasks: Lead AI-powered fashion discovery and shape machine learning systems at ASOS.
- Company: Join ASOS, a global fashion retailer that celebrates individuality and creativity.
- Benefits: Enjoy employee discounts, flexible benefits, and 25 days of annual leave plus a bonus day.
- Other info: Dynamic environment with opportunities for mentorship and career growth.
- Why this job: Make a real impact on how millions discover their personal style using cutting-edge technology.
- Qualifications: Extensive experience in machine learning and strong programming skills required.
The predicted salary is between 63000 - 77000 £ per year.
We're looking for a Principal Machine Learning Engineer to join our Search & Discovery team and help define the technical direction of AI-powered fashion discovery at ASOS. Our mission is to help millions of customers discover outfits that reflect their personal style, preferences and current fashion trends.
As part of the Search & Discovery organisation, we bring together recommendation systems, personalisation, deep learning and large language model (LLM) technologies to create new ways for customers to explore fashion beyond traditional ecommerce experiences.
As a Principal Machine Learning Engineer, you'll shape the technical architecture behind large-scale machine learning systems spanning personalized product & outfit recommendations, conversational agent experiences like AI Stylist, search relevance, style discovery and intelligent product experiences across the customer journey.
You'll work closely with Machine Learning Scientists, Software Engineers, Engineering Leaders and Product Managers, providing technical leadership across Search & Discovery while remaining hands-on with architecture, system design and engineering decisions. This is a highly influential role where you'll help shape the future of machine learning engineering at ASOS while mentoring others and driving engineering excellence across the organisation.
Technical Architecture & System Design
- Own the end-to-end technical architecture for machine learning systems powering personalised fashion experiences, including outfit discovery, homepage ranking and AI Stylist experiences.
- Lead the design and evolution of large-scale batch and real-time machine learning systems serving millions of customers.
- Establish long-term technical direction for recommendation, retrieval and AI-powered discovery platforms.
ML Product Engineering & Production Delivery
- Set technical direction and best practices across recommendation systems, retrieval, personalisation, search relevance, deep learning and generative AI applications.
- Partner with Machine Learning Scientists and Engineering Leaders to translate research and experimentation into robust production systems.
- Identify and resolve architectural, scalability, reliability and performance challenges throughout the machine learning lifecycle.
- Support the delivery of production-grade customer-facing ML products that generate measurable business and customer value.
Technical Leadership & Engineering Excellence
- Provide technical leadership on strategic initiatives, including platform investment decisions and build-versus-buy evaluations.
- Mentor and support Senior, Staff level engineers, helping develop engineering capability across the organisation.
- Promote engineering excellence, modern software engineering practices and responsible adoption of AI-assisted development tools.
- Contribute to technical standards, architectural principles and engineering best practices across multiple teams.
Stakeholder Influence
- Drive the development of shared machine learning capabilities, tools and frameworks used across Search & Discovery and wider engineering teams.
- Represent Search & Discovery engineering in discussions with senior stakeholders, technology partners and business leaders.
Extensive experience designing, building and operating large-scale machine learning systems in production environments. Experience owning and influencing technical architecture across complex engineering ecosystems. A product-focused mindset with a passion for applying machine learning and AI to customer and business challenges.
Extensive experience across the machine learning lifecycle, including data analysis, feature engineering, model development, evaluation, deployment, monitoring and continual improvement. Experience building scalable, observable and highly reliable machine learning services using cloud-based technologies, distributed infrastructure and large datasets.
Technical expertise
- Deep expertise in two or more of the following areas: ranking and relevance, recommendation systems, deep learning, large language models, information retrieval, natural language processing or content understanding.
- Advanced hands-on experience with frameworks such as PyTorch, TensorFlow or similar machine learning frameworks.
- Strong programming skills in Python and/or other languages such as Java or C++.
- Deep understanding of MLOps practices, including deployment, observability, monitoring and lifecycle management at scale.
- Experience building production AI systems using approaches such as retrieval-augmented generation (RAG), agent-based architectures, retrieval systems, model evaluation frameworks and ML-driven scoring approaches.
- Significant experience using AI-assisted engineering tools and coding agents, such as Claude Code, Codex, Cursor or similar technologies, throughout the software development lifecycle.
- Demonstrated experience setting architectural direction, driving engineering strategy and encouraging adoption of technical standards across teams.
- Experience mentoring senior engineers and supporting wider engineering development.
Additional Information
- 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.
Manager, Machine Learning Engineering in London employer: ASOS
ASOS, the parent company of TOPSHOP and TOPMAN, is an exceptional employer that fosters a vibrant and inclusive work culture in the heart of London. With a strong commitment to employee growth, you will benefit from personalised learning opportunities and a supportive environment that encourages creativity and innovation. Enjoy competitive benefits such as generous annual leave, private medical care, and exclusive employee discounts, all while being part of a dynamic team dedicated to shaping the future of fashion.
StudySmarter Expert Advice🤫
We think this is how you could land Manager, Machine Learning Engineering in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at ASOS or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to ASOS.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like ASOS.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like ASOS that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Manager, Machine Learning Engineering in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at ASOS.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at ASOS and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at ASOS
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If ASOS uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.