Pioneering breakthroughs in healthcare by building and maintaining infrastructure to support the end-to-end lifecycle of machine learning experiments for medical devices
Requirements
- Experience with cloud platforms and local/on-prem computer clusters
- Programming skills in C / C++ and at least one other language (preferably Python)
- Familiarity with medical imaging would be beneficial (SPECT, PET, CT, DICOM, coordinate systems, image registration, etc.)
- Familiarity with deep learning libraries
- Knowledge of regulatory requirements for AI medical devices will be beneficial
- Familiarity with different types of databases
Responsibilities
- Designing and implementing scalable, reproducible ML pipelines for research workflows
- Automating data versioning, model tracking, and artifact management
- Containerizing training and inference environments
- Integrating model lineage tracking with research databases
- Collaborating with data scientists to transition research code into production-ready workflows
- Ensuring infrastructure supports compliance, audit logging, and regulatory reporting
- Supporting Continuous Integration (CI)/ Continuous Deployment (CD) workflows for experiments and reproducible reporting
Other
- Minimum of 3 years of experience (advanced degree may be substituted for experience, where applicable) in MLOps, DevOps or data engineering roles
- Bachelor’s or M.S. in Computer Science, Software Engineering or related field
- Authorized to work in the United States without the need for current or future visa sponsorship
