At a Glance
- Tasks: Architect and deploy cutting-edge recommendation systems for personalised health experiences.
- Company: Fast-growing wellbeing platform backed by top investors, redefining health discovery.
- Benefits: Competitive pay, premium wellbeing perks, and a supportive work culture.
- Other info: High ownership role with excellent career growth in a rapidly scaling business.
- Why this job: Join a dynamic team to shape the future of personalised health with AI.
- Qualifications: 4-6 years in Machine Learning, strong Python skills, and experience with recommendation systems.
The predicted salary is between 36000 - 60000 € per year.
A high-growth, investor-backed wellbeing platform is redefining how people discover and experience personalised health. Operating at the intersection of eCommerce, data, and preventative health, the business combines curated products with proprietary health technology including a next-generation blood testing platform to build a truly intelligent wellbeing ecosystem.
The Opportunity
You will architect, train, and deploy recommendation systems that power dynamic merchandising, personalised discovery, and tailored health journeys across web and app. This is a high-impact, cross-functional role working closely with Product, Data, and Engineering to transform behavioural and health data into real-time, intelligent experiences. You will also contribute to predictive systems that anticipate user needs forming the AI intelligence layer behind a category-defining wellbeing platform.
What You’ll Do
- Build and evolve a scalable recommendation engine
- Develop and deploy ML models for ranking, relevance, and engagement
- Experiment with LLM-based retrieval and embedding architectures
- Integrate ML systems into production pipelines with strong MLOps practices
- Drive experimentation, A/B testing, and performance optimisation
- Contribute to predictive models leveraging behavioural and health data
- Champion data quality, ethics, and compliance
What We’re Looking For
- 4–6 years’ experience in Machine Learning or Applied AI (eCommerce or consumer tech preferred)
- Strong Python expertise (PyTorch, TensorFlow or similar)
- Proven experience building recommendation or personalisation systems
- Experience with LLMs, embeddings, and NLP techniques
- Solid SQL and modern data stack experience (e.g. dbt, Snowflake, BigQuery)
- Strong understanding of MLOps, CI/CD, and production monitoring
- Commercially minded, product-oriented, and execution-focused
Why This Role?
- Build foundational AI systems from the ground up
- High ownership and visibility in a scaling business
- Work with ambitious, high-performing teammates
- Competitive compensation and premium wellbeing benefits
Machine Learning Engineer in London employer: DeepRec.ai
Join a high-growth, investor-backed wellbeing platform that is revolutionising personalised health through innovative technology and curated products. As a Machine Learning Engineer, you will enjoy a collaborative work culture that values high ownership and visibility, alongside competitive compensation and premium wellbeing benefits. With ample opportunities for professional growth and the chance to make a significant impact in a rapidly scaling business, this role offers a unique environment for those passionate about transforming health experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to recommendation systems. We love seeing real-world applications of your work, so make sure to highlight any impactful results you've achieved.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in ML and AI. We recommend practicing coding challenges and discussing your thought process out loud, as this can really impress interviewers.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always looking for passionate candidates who are eager to contribute to our mission in the wellbeing space.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Machine Learning Engineer. Highlight your experience with recommendation systems and any relevant projects you've worked on. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you're passionate about personalised health and how your background in ML can contribute to our mission. Keep it engaging and personal – we love a good story!
Showcase Your Projects:If you've got any cool projects or contributions to open-source that relate to ML, don’t hold back! Include links or descriptions in your application. We’re keen to see your hands-on experience and creativity in action.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at DeepRec.ai
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python and frameworks like PyTorch or TensorFlow. Brush up on your knowledge of recommendation systems and be ready to discuss your past projects in detail.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, particularly around building and deploying ML models. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your impact.
✨Understand the Business Context
Familiarise yourself with the wellbeing platform's mission and how machine learning can enhance user experience. Be prepared to discuss how your work can contribute to personalised health journeys and dynamic merchandising.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the role and the company. Inquire about their current ML projects, team dynamics, and how they measure success in their recommendation systems. This demonstrates your enthusiasm and strategic thinking.
