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- Tasks: Join the Outfits Discovery team to enhance machine learning systems and mentor junior engineers.
- Company: ASOS.com, a leading fashion retailer with a focus on innovation.
- Benefits: Employee discounts, paid leave, and tailored learning opportunities.
- Other info: Exciting career growth in a dynamic and collaborative team.
- Why this job: Make a real impact in fashion tech while working in an inclusive environment.
- Qualifications: Experience in deep learning and distributed computing is essential.
The predicted salary is between 60000 - 80000 £ per year.
ASOS.com is seeking a Senior Machine Learning Engineer to join the Outfits Discovery team in Greater London. This role focuses on productionizing machine learning systems, mentoring junior team members, and improving algorithms for pricing and customer targeting.
Key qualifications include:
- Experience in deep learning
- Distributed computing frameworks
- A solid understanding of software development practices
The role offers an inclusive working environment with benefits including employee discounts, paid annual leave, and personalized learning opportunities.
Senior ML Engineer – Fashion Outfit Discovery (High-Scale) employer: ASOS.com
Contact Detail:
ASOS.com Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer – Fashion Outfit Discovery (High-Scale)
✨Tip Number 1
Network like a pro! Reach out to current or former employees at ASOS.com on LinkedIn. A friendly chat can give you insider info and might just get your foot in the door.
✨Tip Number 2
Show off your skills! Prepare a portfolio showcasing your machine learning projects, especially those related to fashion or customer targeting. This will help you stand out during interviews.
✨Tip Number 3
Practice makes perfect! Brush up on your technical interview skills by doing mock interviews with friends or using online platforms. Focus on deep learning and distributed computing questions.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the ASOS team.
We think you need these skills to ace Senior ML Engineer – Fashion Outfit Discovery (High-Scale)
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with deep learning and distributed computing frameworks in your application. We want to see how your skills align with the role, so don’t hold back!
Tailor Your Application: Take a moment to customise your CV and cover letter for this specific role. Mention how your past experiences can contribute to improving algorithms for pricing and customer targeting at ASOS.com.
Be Yourself: We value authenticity! Let your personality shine through in your written application. Share your passion for machine learning and how you envision contributing to our inclusive working environment.
Apply Through Our Website: Don’t forget to submit your application through our website. It’s the best way for us to receive your details and get the ball rolling on your journey with StudySmarter!
How to prepare for a job interview at ASOS.com
✨Know Your ML Stuff
Make sure you brush up on your deep learning and distributed computing frameworks. Be ready to discuss specific projects where you've implemented these technologies, as well as the challenges you faced and how you overcame them.
✨Show Off Your Mentoring Skills
Since this role involves mentoring junior team members, think of examples where you've successfully guided others. Prepare to share how you approach teaching complex concepts and fostering a collaborative environment.
✨Understand ASOS's Business
Research ASOS and its approach to fashion outfit discovery. Familiarise yourself with their algorithms for pricing and customer targeting, and be prepared to discuss how your skills can enhance their existing systems.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the role and the company culture. Inquire about the team's current projects, the tools they use, and how they measure success in their machine learning initiatives.