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NLP Engineer @ Lynxcare



1.     Position content

1.1     Purpose of the position

The NLP team builds new NLP components with the newest technologies and support Operations doing customer projects. 
The NLP Engineer will be part of a growing NLP team that develops and deploys new algorithms to extract information from clinical narratives with the highest accuracy.

1.2    Reporting

The NLP Engineer reports to the NLP team lead. In general all NLP team members work together in the team, but also have frequent collaboration with the data engineering team, the annotation team, and the business consultant.

1.3    Tasks and responsibilities

•    Contribute to the development of new NLP components, making use of the latest AI technologies (example: Recently, a new Entity Linker component was developed that linked text strings in multiple languages to UMLS codes with high accuracy and at high speed).

•    Train models for new use cases in several languages.

•    Deploy models to Azure cloud.

•    Support Operations with finetuning models for new client (During finetuning a fully connected neural network is trained that adapts the results of the pretrained network to the results of a given customer).

•    Stay up to date with latest innovations in NLP (This can be done through reading of books or research papers and following courses).

•    Continuously improve the Machine Learning processes for a better monitoring of the model development (Lynxcare wants to know the evolution of the precision and recall of models when training data of new customers are added. The team wants to follow these data per entity type).

•    The NLP engineer will work mostly with the other members of the team, the annotators and the consultants in Operations. The collaboration is rather complex. Difficulties are triggered when the models are lacking in quality for unknown reasons and when there are too many customer projects. These issues need to be solved by a better NLP pipeline and by a better training workflow (NLP architect).

1.4    Reason for the job opening

LynxCare’s business is growing fast. To better support this growth the NLP team is extended with several NLP Engineers.

1.5    Expectations first 12 months

Given that Lyncare has prepared everything for the new NLP engineers, it is expected that the NLP pipeline is transformed to the newest technology in 12 months. Lynxcare expects that the main components (e.g., Entity Linker, relation extractor) have been replaced within 6 months.

2.    Technical and business challenges of LynxCare

2.1    Technical challenges:

·       Short & middle term: Make the platform more scalable, both in terms of decreasing implementation-time (i.e. time to onboard new clients), as well as in terms of reducing data-processing time (performance optimization), and providing more standardized data-uploading functionalities (aligned with international standards for data-transfer in the healthcare industry (eg. HL7)).

·       Longer term: Extend the configurability & usability of the platform towards end-users (to enable more user-interaction with the platform, and a move towards client self-service).

2.2    Business challenges:

●    International expansion into other EU-markets, with a first focus on France and Germany.
●    International expansion into the USA.

2.3    Technology context

2.4    Technology stacks

●    4 technology-stacks in parallel:
o    Web-development (Java, NodeJS)
o    NLP & Data Science
o    Data Engineering (MS Databricks, MS Powerplay, DBT, Terraform)
o    Cloud-infrastructure (MS-Azure).
●    Highly complex systems-architecture, because of the combination and inter-twinedness of these 4 technology stacks.




  • At least 2 years experience with training and deploying ML/NLP models, preferably in the healthcare sector.
  • Solid and proven working experience with machine learning, deep learning, and natural language processing.
  • Experience with one or more of the following libraries: PyTorch, TensorFlow, JAX, HuggingFace, spaCy.
  • Expert proficiency in Python and its data science stack: NumPy, Pandas, sciKit-learn, …
  • Experience with Azure cloud and MLFlow is a plus.
  • Dedicated/committed to quality as well as timely execution. High sense of responsibility.
  • Being open, cards on the table, honest, respectful in communication and not political. 
  • Enjoyable personality to collaborate with.
  • Languages: English 


✔    Attractiveness of the sector (Healthcare, societal impact).
✔    Considered as one of the companies in the market that is shaping the sector/industry of Real World Evidence.
✔    Small, fast growing company, not a large corporate filled with internal politics. You will be part of a young team of 30+ colleagues, consisting of a mix of clinicians and data scientists, and you will work on challenging projects together with physicians and pharmaceutical companies.
✔    You will have the opportunity to help shape the company’s future.
✔    Most advanced in terms of technical capabilities (this is what customers are telling). A strong technical and operational team with extensive knowledge of the technology and the environment in the industry.
✔    The NLP architect can grow to NLP Architect.
✔    Hybrid working; partly in the office and partly work-from-home.
✔ complete and competitive salary package with company car and fringe benefits
✔    Trainings & seminars with your team.


NLP Engineer @ Lynxcare

LynxCare, with offices in Belgium (Leuven) and the United States (New York), is a Belgian Healthtech  scale-up and big data specialist in healthcare, which developed a AI- and NLP  powered SaaS clinical (all-in-one) data platform for hospitals and for life sciences research.

Lynxcare is currently available in Belgium, The Netherlands and the US. The company is continuously developing new use cases covering more disease areas (with spearheads in Cardiology and Oncology) and additional languages (currently active in English, Dutch, French, and German) thereby widening its market reach, aiming at expanding into the French & German market as of 2022.

Examples of customers are: UZA, UZ Leuven, ASZ Aalst, AZ Maria Middelares, AZ Geel, AZ Groeninge, Astra Zeneca, Takeda, Pfizer, ARJO, Janssen, Bayer,… 

LynxCare's platform assists hospitals with time-consuming and complex patient data processing and hospital operations. The AI platform offers benefits to patients, physicians and healthcare staff alike. Silos are broken, data is made more insightful and the platform provides more structure. The platform allows insights to be drawn across the multitude of databases and data structures in the hospital. From structured laboratory studies to unstructured reports (with NLP). Patients also get much better insights into the success rate of certain procedures.

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