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- Tasks: Design and optimise RAG pipelines for healthcare data using large language models.
- Company: Join all.health, a leader in revolutionising patient care with innovative wearable technology.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact on healthcare while working in a dynamic, collaborative environment.
- Qualifications: 3+ years in machine learning/NLP, strong Python skills, and experience with healthcare data.
- Other info: Stay ahead in GenAI research and contribute to scalable deployments.
The predicted salary is between 48000 - 84000 £ per year.
all.health is at the forefront of revolutionizing healthcare for millions of patients worldwide. Combining more than 20 years of proprietary wearable technology with clinically relevant signals, all.health connects patients and physicians like never before with continuous, data-driven dialogue. This unique position of daily directed guidance stands to redefine primary care while helping people live happier, healthier, and longer.
About the Role
You will design, build, and optimize RAG pipelines that combine large language models (LLMs) with domain-specific retrieval systems, enabling natural language understanding and reasoning over patient data, clinical guidelines, and health records. Your work will directly impact how patients and clinicians interact with our platform, enabling safe, accurate, and context-aware content surfacing.
Responsibilities
- Design and implement RAG architectures using open-source and potentially proprietary LLMs (e.g., LLaMA, Mistral, OpenAI, Anthropic).
- Build and maintain retrieval pipelines over structured and unstructured health data (EHRs, patient notes, device logs, clinical documentation).
- Develop indexing strategies using vector databases (e.g., FAISS, Weaviate, Pinecone) and embedding models (e.g., BioBERT, ClinicalBERT).
- Integrate RAG outputs into user-facing applications, ensuring responses are grounded, reliable, and privacy-compliant.
- Work closely with product, clinical, and data science teams to fine-tune prompts, evaluate responses, and iterate on model performance.
- Build evaluation pipelines for factuality, relevance, and safety using synthetic and real-world datasets.
- Contribute to infrastructure for scalable GenAI deployments and model versioning.
- Stay up to date with the latest research in GenAI and health tech applications of LLMs.
Requirements
- 3+ years of experience working in machine learning / NLP roles, with recent focus on LLMs and/or GenAI.
- Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries (LangChain, LlamaIndex, Transformers).
- Hands-on experience with vector search, embedding models, and retrieval pipelines.
- Familiarity with prompt engineering, prompt tuning, and evaluation of generative model outputs.
- Experience working with healthcare or sensitive data (HIPAA/GDPR compliance awareness).
- Strong problem-solving skills and ability to move fast in a startup environment.
- Bonus: Experience with MLOps, Kubernetes, AWS/GCP, and deploying models in production.
GenAI Engineer - Retrieval-Augmented Generation (RAG) employer: all.health
Contact Detail:
all.health Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land GenAI Engineer - Retrieval-Augmented Generation (RAG)
✨Tip Number 1
Familiarise yourself with the latest advancements in retrieval-augmented generation (RAG) and large language models (LLMs). This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the healthcare tech space, especially those working with GenAI. Attend relevant meetups or webinars to gain insights and potentially get referrals that could boost your application.
✨Tip Number 3
Showcase your hands-on experience with vector databases and embedding models through personal projects or contributions to open-source initiatives. This practical knowledge can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss how you would approach integrating RAG outputs into user-facing applications. Think about real-world scenarios and be ready to share your ideas on ensuring privacy compliance and reliability in your solutions.
We think you need these skills to ace GenAI Engineer - Retrieval-Augmented Generation (RAG)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, NLP, and specifically with large language models. Emphasise any projects or roles that involved RAG architectures or healthcare data.
Craft a Compelling Cover Letter: In your cover letter, express your passion for revolutionising healthcare through technology. Mention specific experiences that align with the responsibilities of the role, such as building retrieval pipelines or working with embedding models.
Showcase Technical Skills: Clearly outline your proficiency in Python, deep learning frameworks, and GenAI libraries in your application. Provide examples of how you've used these skills in past projects, especially in relation to healthcare or sensitive data.
Highlight Collaboration Experience: Since the role involves working closely with product, clinical, and data science teams, include examples of successful collaborations in your application. This could be projects where you fine-tuned models or integrated outputs into user-facing applications.
How to prepare for a job interview at all.health
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning, NLP, and specifically large language models. Highlight any projects where you've designed or optimised RAG pipelines, and be ready to explain the technical details in a clear and concise manner.
✨Demonstrate Your Problem-Solving Abilities
Expect questions that assess your problem-solving skills, especially in a fast-paced environment. Prepare examples from your past work where you successfully tackled challenges related to healthcare data or model performance.
✨Understand the Healthcare Context
Familiarise yourself with the healthcare industry, particularly how technology is transforming patient care. Being able to discuss the implications of your work on patient outcomes will show your commitment to the role and the company's mission.
✨Prepare for Collaborative Scenarios
Since the role involves working closely with product, clinical, and data science teams, be ready to discuss your experience in collaborative settings. Share examples of how you've effectively communicated and worked with cross-functional teams to achieve common goals.
