Machine Learning Engineer (Recommendations)
Role details
Job location
Tech stack
Job description
We're looking for a Machine Learning Engineer - Recommendations to help build the foundation of Healf's personalisation and intelligence platform.
You'll design, train, and deploy recommendation models that power dynamic merchandising, personalised discovery, and tailored health journeys across web, app, and beyond.
This is a highly cross-functional role working closely with Product, Data, and Engineering to turn raw data into real-time insights and experiences. Over time, you'll also contribute to developing predictive algorithms that help users make better health decisions - forming the intelligence layer of Healf's long-term vision: a wellbeing platform powered by AI and data.
Experience with LLMs, embedding models, and applied AI systems will be highly valuable as we evolve towards conversational and contextual recommendation systems., * Build and evolve Healf's recommendation engine, driving personalised product discovery and dynamic merchandising across web and app.
- Develop and deploy machine learning models that optimise product relevance, content ranking, and user engagement.
- Partner with Product and Data teams to define and capture the signals that power our personalisation logic.
- Contribute to the development of predictive algorithms that leverage data from Healf Zone, Helix, and user behaviour to anticipate customer needs.
- Collaborate with Engineering to integrate ML systems into production pipelines and ensure scalable performance.
- Experiment with LLM-based retrieval and recommendation architectures
- Continuously measure, evaluate, and optimise model performance through experimentation and A/B testing.
- Help shape the roadmap for Healf's broader wellbeing intelligence platform - connecting data, health insights, and user intent.
- Champion data quality, ethics, and compliance in all model design and deployment processes.
Requirements
- 4-6 years of experience as a Machine Learning Engineer or Data Scientist, ideally within eCommerce, consumer tech, or recommendation systems.
- Strong background in building and deploying ML models using Python, PyTorch, TensorFlow, or similar frameworks.
- Proven experience with recommendation engines, ranking algorithms, or personalisation pipelines.
- Familiarity with LLMs, embeddings, and NLP techniques for recommendation and content matching.
- Proficient in SQL and data manipulation tools; experience working with modern data stacks (e.g., dbt, Snowflake, BigQuery).
- Solid understanding of MLOps practices - model versioning, CI/CD, and production monitoring.
- Comfortable working across product and engineering teams to translate business goals into model objectives.
- Experience with experimentation, A/B testing, and performance measurement.
- Curious, self-directed, and excited to build the intelligence layer behind the future of personalised wellbeing.