ML Software Engineer

Responsibilities

In your role you will support the development of internal ML tooling and working towards productization of prototypic workflows into compliant ML products at scale.

• Support development of internal ML tooling
• Develop novel, or improve existing tools
• Add new features, fix bugs, raise test coverage and improve documentation
• 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 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)
• Software engineering best practices, the aim of writing high performant, modern, bug free code, proper documentation of code, unit-testing, code quality, etc. (required)
• PyTorch (required good working knowledge)

Desirable Skills or Experiences

• Experiment tracking platforms such as Weights & Biases, MLFlow (past experiences)
• Processing platforms, such as Kubeflow (optional)
• Computer Vision problems (recommended), e.g. experience with OpenCV (optional)
• Cloud computing infrastructure, in particular AWS (recommended)
• Working in a regulated environment, developing code under pre-set regulatory rules (nice to have)