Job Description:

In your role, you will support the development of our internal ML tooling for large image analysis in the medical domain. Furthermore you will be working towards productization of prototypic workflows into compliant ML products at scale.

- Support development of internal ML tooling

o Develop novel, or improve existing tools

o Add new features, fix bugs, raise test coverage and improve documentation

- Develop, deploy and maintain complex data stream pipelines.

- Evaluate, qualify and integrate cloud-based computing platforms in close collaboration with IT and development teams.

- Help defining workflows, evaluate tooling and infrastructure, aid users in test scenarios

- Ensure compliance of processes and developed tooling

Must-have Skills or Experiences

- Experience in building and applying ML (not hosted LLM) systems with frameworks such as PyTorch, TensorFlow, JAX

- Experience in building training data processing pipelines for image data

- Experience in in MLOps lifecycles (model training, model validation, model deployment, quality monitoring) *Though we would also consider similar experiences to the above, for instance building data processing pipelines for proteomics data instead of image data, etc.

- Python (excellent, required), - Experience with MLOps (W&B, MLFlow) and deep learning frameworks a plus (PyTorch, PyTorch Lightning, …)

- Software engineering best practices, the aim of writing high performant, modern, bug free code, proper documentation of code, unit-testing, code quality, etc. (required) - Good working knowledge of Linux environments, Git-based workflows, GitHub Actions, containerization (Docker) and orchestration (Kubernetes)

- At least several years of experience as DevOps

- Experience with MLOps (W&B, MLFlow) and deep learning frameworks a plus (PyTorch, PyTorch Lightning, …)

Desirable Skills or Experiences

- TypeScript/Node/JavaScript (desirable) - Processing platforms, such as Argo Workflows or Kubeflow (desirable)

- Computer Vision problems (recommended), e.g. experience with OpenCV (optional)

- Distributed microservice architectures (preferred)

- Cloud computing infrastructure, in particular AWS (preferred)

- Working in a regulated environment, developing code under pre-set regulatory rules (optional)