Machine Learning Engineer
We live in a world where most information is readily available at our fingertips. You might be surprised to learn that the situation in hospitals is rather different. The systems used for electronic healthcare records (EHR) facilitate the clinical workflow, retrieving and storing information for each patient. There is, however, a large need for secondary usage of this data. Think of linking patients to clinical trials, medical research studies, and care quality assessment. For this purpose CTcue has developed a search engine that enables medical professionals to find patient cohorts and collect data. In all that we do, we have the doctors and patients at the fore of our mind, and we also make sure to strictly adhere to privacy regulations. CTcue is currently used on a daily basis by 30+ hospitals in The Netherlands and in Belgium.
CTcue is a scale-up, which means there is a lot of room to make your own mark and propose and work out creative solutions. The direction and view are largely determined. There is a clear vision on what we are as a company. The initial versions of the product have passed and now there is an established market for our product. The atmosphere has a start-up feel with great team spirit based on collaboration and chasing a goal together. There is little hierarchy, making everyone in the company approachable.
What are you going to do?
As a machine learning engineer you will apply machine learning algorithms to the unstructured data of EHRs (notes, referral letters, questionnaires), with the goal to get ever closer to a perfect representation of a patient’s medical record. EHR data is highly messy, so expect to get your hands dirty on this. Our data pipeline and infrastructure is well established, so you will be provided with the necessary resources (including GPUs) to implement state of the art models.
Together with your colleagues you will form a small, cosy and self-organising team. As a team you are responsible for building the infrastructure, implementing the algorithms and converting them to production ready models. Additionally, you will be part of a larger, cross-functional and dynamic team consisting of developers, designers, medical consultants and data engineers.
Medical data is an interesting problem space, both in complexity and in societal value. The texts are a domain of its own with extremely high information density. It gives a real kick when users let us know they were able to collect data that they were unable to collect without CTcue, such as finding people with a rare disease that might take years otherwise to diagnose or rapidly building quality datasets for COVID research.
You are a professional who is passionate about improving healthcare and having a real impact. A background in Artificial Intelligence is necessary. You need to be able to independently understand which algorithms best address a problem. You have an appetite to learn and to develop yourself, an innate curiosity, and you can bring to light clarity around abstract and unclear problems. Furthermore, you are capable of understanding the context in (medical) data and deriving meaning from it. You feel comfortable working in a self-organising company where there is little hierarchy.
- You have relevant industry experience of at least 3 years
- You have the software engineering skills to implement and integrate AI components in our infrastructure yourself. Models are not “tossed to the neighbors” for implementation. Experience with Python is a plus.
- You have knowledge of and a love for NLP tasks.
- You understand and are able to implement a wide range of models/techniques for supervised and unsupervised settings. You have at least some experience with deep learning.
- You are able to read and understand Dutch at a level that makes, for instance, assessing the context in medical text possible.
- You can describe and speak in an approachable way about complex analyses and concepts within a cross-functional team. You are a great “analytic translator”.
Do you want to learn more about the position or about how to apply? Contact Lydia Mennes for more information: firstname.lastname@example.org