We are looking for a Machine Learning Engineer to help turn proven scientific and modeling work into reliable, maintainable production code. This is an applied ML engineering role, not focused solely on model research or MLOps/platform ownership. You will collaborate with Process Model Scientists to understand validated modeling ideas and incorporate them into the ML codebase in a clean, reusable, and testable manner. In our domain, model changes are assessed not just by generic ML metrics but also by their impact on scientific outputs and biological constraints.

The Role

You will be responsible for productionizing validated work from Process Model Scientists into maintainable ML code and integrating ML models into production workflows. Your tasks will include:

• Enhancing the ML codebase structure for faster future use cases.
• Designing reusable abstractions for common modeling patterns.
• Implementing feature transformations and supporting feature design.
• Building internal tools or frameworks to reduce repetitive work.
• Proposing new ML ideas or architecture improvements to facilitate Process Model Scientists' work.
• Debugging model-related issues and supporting regression analysis.
• Collaborating with the team on orchestration and platform-related challenges.

Requirements

• Strong Python and software engineering skills.
• Experience in building maintainable production code for ML or model-driven applications.
• Good understanding of ML workflows including training, evaluation, and validation.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Ability to reason about feature transformations and model behavior.
• Understanding of systems design in software and the needs of Process Model Scientists.
• High autonomy in solving unclear problems and improving systems.
• Ability to work remotely with at least 4 hours of overlap with CET working hours.
• B.S. in Engineering, Computer Science, or a related technical field.

Nice to Have

• Experience with hybrid modeling or scientific ML workflows.
• Familiarity with tools like Flyte, Kubeflow, MLflow, or similar.
• Interest in reinforcement learning or explainable AI.

This role is focused on production ML code, transforming validated scientific work into reusable software. If you are passionate about applied ML and want to work closely with scientists and engineers, we would love to hear from you.