Machine Learning Engineer, Cardiac Sciences
Mentions vibe coding as a plus to maximize productivity; not required but encouraged.
About the Role
The Machine Learning Engineer will develop, validate, deploy, and maintain machine learning models and curated health datasets to support research projects in the Data Intelligence for Health (DIH) Lab. This fixed-term, part-time role (approx. 6 months) supports clinical research workflows and collaborates with researchers and stakeholders to produce reproducible, production-level ML artifacts.
Job Description
Role
Machine Learning Engineer (Fixed Term, Recurring Part-time — ~6 months) reporting to the Principal Investigator in the Data Intelligence for Health (DIH) Lab at the Cumming School of Medicine, University of Calgary. The role focuses on developing, validating, deploying, maintaining, and documenting machine learning models and curated multi-modal health datasets for research projects.
Key Responsibilities
- Develop, validate, deploy, and maintain production-level machine learning models.
- Curate, organize, and maintain multi-modal health datasets (tabular, imaging, text, time series).
- Meticulously document and version control ML source code and models.
- Engage with researchers, stakeholders, and end-users to align solutions with needs.
- Assist with publications and presentations of research findings.
- Manage time and report progress to the PI; maintain productive relationships within the project team and campus community.
- Demonstrate communication, teamwork, and judgement within established guidelines.
Requirements
- Bachelor’s degree in computer science, engineering, or a relevant field.
- 1–2 years of work experience in machine learning.
- Advanced proficiency with: Python, PyTorch, TensorFlow, Keras, scikit-learn, SQL, Docker, and GitHub.
- Strong understanding of deep learning concepts, best practices, and MLOps.
- Experience with deep learning foundation models (stated as essential).
- Familiarity with Agile software development and the Scrum framework.
- Understanding of application and data security.
- Strong documentation, version control, communication, teamwork, project management, and problem-solving skills.
- Ability to work independently with limited supervision.
Assets / Preferred
- Experience with AWS SageMaker.
- Experience with high-performance computing (HPC).
- Health research and development experience in an academic environment.
- Ability to utilize vibe coding to maximize productivity (listed as a plus).
Additional Information
- Appointment type: Fixed Term Recurring Part-time (~6 months, possible extension based on funding).
- Application deadline: April 23, 2026.
- This position is part of the AUPE bargaining unit (Technical Job Family, Phase 1).
- Preference will be given to Canadian citizens and permanent residents; accommodations available for applicants with disabilities.