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- Tasks: Lead engineering efforts in serving personalisation models and enhance recommendation systems.
- Company: Leading technology company focused on innovation and user engagement.
- Benefits: Remote work options, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact by improving user experiences with cutting-edge machine learning.
- Qualifications: Expertise in machine learning, coding skills in Go or Java, and cloud platform experience.
- Other info: Join a dynamic team with a focus on complex ML systems and user engagement.
The predicted salary is between 57600 - 84000 £ per year.
A leading technology company is seeking an experienced Staff Engineer to lead engineering efforts in serving personalization models at scale. This role requires expertise in machine learning, strong coding skills in Go or Java, and experience with cloud platforms like AWS. The ideal candidate will have a Master's or PhD and at least 8 years of industry experience.
Responsibilities include:
- Guiding the architecture of complex ML systems
- Improving recommendation systems to boost user engagement
The position is based in London with remote work options.
Staff ML Engineer: Recommender Systems (Remote/London) employer: Jobster
Contact Detail:
Jobster Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff ML Engineer: Recommender Systems (Remote/London)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those working with ML and recommendation systems. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to ML and personalisation models. This is your chance to demonstrate your coding prowess in Go or Java and your understanding of cloud platforms like AWS.
✨Tip Number 3
Prepare for interviews by brushing up on system design and architecture. Be ready to discuss how you would improve recommendation systems and boost user engagement. We want to see your thought process!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Staff ML Engineer: Recommender Systems (Remote/London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning and coding in Go or Java. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Tell us why you’re passionate about recommender systems and how your background makes you the perfect fit for this role. Keep it engaging and personal.
Showcase Your Experience: With at least 8 years of industry experience, we want to see specific examples of your work. Highlight any projects where you’ve improved recommendation systems or worked with cloud platforms like AWS.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Jobster
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially around recommender systems. Be ready to discuss your past projects and how you've applied ML techniques to solve real-world problems.
✨Show Off Your Coding Skills
Since strong coding skills in Go or Java are a must, practice coding challenges in these languages. Be prepared to write code during the interview and explain your thought process clearly.
✨Cloud Knowledge is Key
Familiarise yourself with AWS and any relevant cloud services. Be ready to discuss how you've used cloud platforms to deploy ML models and scale applications effectively.
✨Engagement Strategies Matter
Think about how you've improved user engagement in previous roles. Prepare examples of how your work has directly impacted user experience and be ready to share your insights on enhancing recommendation systems.
