At a Glance
- Tasks: Architect and deploy cutting-edge recommendation systems for personalised health experiences.
- Company: Fast-growing wellbeing platform transforming health discovery with innovative technology.
- Benefits: Competitive pay, premium wellbeing perks, and a supportive work environment.
- Other info: High ownership role with excellent career growth in a dynamic startup.
- Why this job: Join a mission-driven team and shape the future of personalised health.
- 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. Backed by leading consumer investors and scaling rapidly, the company is now hiring a Machine Learning Engineer (Recommendations) to build the backbone of its personalisation engine.
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 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 have the opportunity to build impactful AI systems in a collaborative environment that values employee growth and wellbeing, offering competitive compensation and premium benefits. This role not only allows you to work with talented professionals but also places you at the forefront of transforming health experiences for users.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨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 want to see your work in action, so make it easy for hiring managers to see what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML concepts. Practice coding challenges and be ready to discuss your past projects. We believe that being well-prepared can set you apart from the competition.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our mission to redefine wellbeing.
We think you need these skills to ace Machine Learning Engineer
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 built any ML models or recommendation systems, don’t hold back! Include links to your GitHub or any live demos. We’re keen to see your work in action and understand your thought process behind it.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it’s super easy!
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 how you overcame obstacles.
✨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 not only shows your enthusiasm but also helps you gauge if it’s the right fit for you.
