Applied AI Engineer

Your mission is to help build the next frontier in AI for healthcare

To meet this challenge, we must use the massively growing amount of healthcare data to support clinical decision-making. We are solving this by translating various data streams into clinically- actionable insights that support overstretched clinicians identify patient health changes earlier. Over time, we are personalising this to ultimately build your human digital twin.

This is a “right time, right place” kind of moment, with increasing momentum, multiple hospital partnerships, vast data access, a first product in clinic with early positive results, and imminent regulatory approval (Class 2 EU:MDR). Now, we are looking for you to take us to the next level.

We are building a company that wants to change the way healthcare innovation is brought to market and are looking for people to help us do just that. Are you up for the challenge?

About the Role

We work at the bleeding edge of AI for healthcare, translating multimodal, complex and challenging data streams into actionable clinical insights that have a real-world impact.

• Design and deploy machine learning pipelines focused on multimodal clinical data spanning electronic health records, wearables, clinical images, genomics, etc.
• Collaborate closely with a cross-functional team to understand requirements, curate datasets, and implement models into production.
• Stay updated with the latest advancements in foundational machine learning to drive innovation within the company.

If you are passionate about the confluence of AI, healthcare, and pioneering technological solutions, we'd love to meet you!

Must Have:

• Advanced degree in a quantitative or engineering discipline such as computer science, engineering, mathematics, computational biology, bioinformatics, etc., OR equivalent professional work experience.
• Proven experience in deploying machine learning and deep learning models, preferably in the health domain.
• Strong programming and algorithmic skills, specifically in Python, C++, or other relevant languages.
• Familiarity with fundamental concepts of deep learning and transformer-based architectures, including scaling and fine-tuning models over large-scale data sources.
• Excellent communication skills and ability to work closely with interdisciplinary teams.

Nice-to-Have:

• Understanding of AI ethics, responsible AI practices, and regulatory considerations
• Experience with agile ways of working, devops, version control, unit testing, CI/CD pipelines, model monitoring, and reproducibility. We use Python, SQL, Unix-based systems, git, and github for collaboration and review, including Hugging Face, Hydra, MLFlow, DVC, etc., for experiment tracking and monitoring.
• Experience working with the OMOP Common Data Model and working with Standard Medical Vocabularies, such as SNOMED, ICD10. Knowledge of other clinical data standards like HL7, FHIR, and CDA would be advantageous. An understanding of healthcare workflows and medical terminologies can be a significant asset for this role.