Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Build cutting-edge recommendation systems and take ownership of ML functions.
- Company: Exciting Series A Scale-Up in the Retail space with a focus on innovation.
- Benefits: Competitive salary up to £150k, equity, and hybrid working in Central London.
- Other info: Perfect for ML enthusiasts looking for growth in a dynamic environment.
- Why this job: Join a passionate team and make a real impact in personalisation features.
- Qualifications: Strong ML background and experience with recommendation/ranking systems.
The predicted salary is between 90000 - 150000 £ per year.
OB have partnered with a Series A Scale-Up in the Retail space, who are looking to expand their ML function as they continue to scale their platform. You will take a high degree of ownership over the ML function, building solutions for personalisation features on the platform.
This is a perfect role for a true ML enthusiast, where you'll have the opportunity to build Recommendation and Ranking systems in production environments, where you will use your foundation of Data Science and ML to work closely with the wider team.
Key skills and experience:
- Strong background in Machine Learning
- Experience building or working on Recommendation/Ranking systems
- Ideally experience in scaling organisations
- Strong academic background in STEM, from a Top ranked University
Salary - Base salary of £90k-£150k + equity (depending on skills and experience)
Hybrid working in Central London
To be considered, you must be UK based and Visa sponsorship is unavailable.
ML Engineer - Recommender Systems - Up to £150k in London employer: Oliver Bernard
Contact Detail:
Oliver Bernard Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer - Recommender Systems - Up to £150k in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with ML enthusiasts on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to recommendation and ranking systems. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common interview questions and work on real-world problems to demonstrate your expertise during the interview process.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace ML Engineer - Recommender Systems - Up to £150k in London
Some tips for your application 🫡
Show Your Passion for ML: When writing your application, let your enthusiasm for machine learning shine through. Share specific projects or experiences that highlight your love for building recommendation and ranking systems. We want to see that you’re not just qualified, but genuinely excited about the work!
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter for this role. Highlight relevant experience in ML and any specific projects related to recommendation systems. We appreciate when candidates take the time to align their skills with what we’re looking for!
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language and avoid jargon unless it’s necessary. We value clarity, so make it easy for us to see why you’d be a great fit for our team!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Oliver Bernard
✨Know Your ML Fundamentals
Brush up on your machine learning fundamentals, especially around recommendation and ranking systems. Be ready to discuss algorithms you've used, their pros and cons, and how they can be applied in real-world scenarios.
✨Showcase Your Projects
Prepare to talk about specific projects where you've built or contributed to recommendation systems. Highlight the challenges you faced, the solutions you implemented, and the impact your work had on the overall project.
✨Understand the Company’s Product
Research the company’s platform and its current personalisation features. Think about how you could enhance their existing systems or introduce new ideas that align with their goals. This shows your enthusiasm and initiative.
✨Ask Insightful Questions
Prepare thoughtful questions about their ML function and future plans. Inquire about the tools they use, their data sources, and how they measure success. This not only demonstrates your interest but also helps you gauge if it’s the right fit for you.