About ENPICOMENPICOM brings together cross-domain expertise in biology, software, and AI to simplify complex workflows into intuitive solutions that scientists love to use. We build the scalable digital platform that provides the foundation of harmonized, actionable data necessary to operationalize AI. By empowering scientists to easily access and apply AI models for analysis and decision-making, we enable them to develop breakthrough medicines faster and more effectively.
The role
We are looking for a Machine Learning Scientist to join our Bioinformatics & AI team. You will help develop machine learning models that speed up the discovery and optimization of therapeutic antibodies, and you will help turn those models into reliable tools that our customers use every day on the ENPICOM platform.
This role is a good fit if you enjoy both sides of the work: understanding a scientific problem deeply, and making sure the solution actually runs well in practice. You will also help extend the AI capabilities of the platform, including newer approaches such as AI agents that can carry out analysis steps on a scientist's behalf.
You will work closely with experienced ML scientists, bioinformaticians and software engineers, and you will have room to grow your skills in antibody biology, model deployment, and modern AI tooling.
What you will do
- Evaluate, adapt, and train deep learning models for antibody engineering tasks, such as predicting antibody properties or suggesting improved protein variants.
- Compare published and in-house models on our data and help decide which ones are worth bringing to the platform.
- Turn promising models into robust, tested services: packaging, versioning, monitoring, and keeping them running well over time.
- Help build AI features on the ENPICOM platform, including agent-based tools that connect models and analysis workflows.
- Design sound experiments and use statistics to check whether a model really works, and where it does not.
- Work with bioinformaticians and scientists to understand the biology behind a problem and to explain results clearly to non-specialists.
- Keep up with new methods in machine learning for proteins and antibodies, and share what you learn with the team.
- Mentor junior colleagues and interns, helping them grow their skills and sharing what you know.
What we offer
- Work that matters: your models help bring better antibody medicines to patients.
- A small, friendly team where your work quickly makes it into a product used by scientists around the world.
- A competitive salary based on your experience.
- Location: 's-Hertogenbosch, the Netherlands, hybrid with 1 to 2 days per week in the office.
What you bring
- An MSc or PhD in machine learning, computer science, bioinformatics, computational biology, physics or a related field, or equivalent hands-on experience.
- Experience applying machine learning or deep learning to real problems, in industry or academia.
- Solid Python skills and hands-on experience with a deep learning framework such as PyTorch.
- A good grasp of statistics and how to evaluate models honestly: data splits, baselines, uncertainty and common pitfalls.
- Interest in biology and the motivation to learn the antibody side of the work. Prior knowledge of immunology is welcome but not required.
- Good software habits: version control, readable code and testing.
- Clear communication in English, and a curious, collaborative attitude.
- Eligibility to work in the Netherlands, and the ability to come to our office in ‘s-Hertogenbosch 1 to 2 days per week
Nice to have
- Experience with MLOps: tools for tracking experiments, serving models, containers (such as Docker) or cloud platforms.
- Experience building with large language models or agent frameworks, for example tool use or the Model Context Protocol (MCP).
- Familiarity with protein language models or other models for protein sequences and structures.
- Experience with antibody or immune repertoire data, or with bioinformatics tools for sequence analysis.
- Experience with workflow systems for running analysis pipelines.
