Machine Learning Engineer - Cardiovascular Epigenetics & Genetics
About Cardio Diagnostics
Cardio Diagnostics is a precision cardiovascular medicine company committed to making cardiovascular disease prevention and early detection more accessible, personalized, and precise. Our mission is to advance and commercialize our proprietary AI-driven Integrated Epigenetic-Genetic Engine™ (“Core Technology”) for cardiovascular disease, establishing ourselves as a leading medical technology company focused on improving prevention, diagnostics, and treatment in the field of cardiovascular health.
Responsibilities
We are seeking a full-time, on-site Machine Learning Engineer to join Cardio Diagnostics. In this role, you will play a key part in developing our advanced platform and drive impactful machine learning initiatives across multiple disciplines, including operations, modeling, and data engineering. As a member of a small, agile team, you’ll take ownership of end-to-end machine learning projects. Key responsibilities include:
- Designing, developing, and maintaining data pipelines and machine learning models
- Collaborating with scientists and engineers to create machine learning solutions that address complex healthcare challenges
- Implementing MLOps best practices, such as model performance tracking, data and model drift detection, and feedback loops for continuous improvement
- Leveraging cloud infrastructure to scale and deploy machine learning models
- Ensuring high-quality outcomes by adhering to coding standards, conducting regular code reviews, and following best practices in software development
- Using tools such as bug tracking, code reviews, and version control for efficient project management
- Analyzing and integrating large, diverse clinical and molecular datasets to generate actionable insights
- Documenting, summarizing, and presenting results to both technical and non-technical stakeholders
Qualifications
- MS or PhD in a quantitative discipline (e.g., computer science, biomedical informatics, machine learning, statistics, computational biology, applied mathematics, or a related field)
- Strong communication skills, with the ability to translate complex technical concepts to diverse audiences
- Self-motivated and adaptable, with a proven ability to quickly learn and apply new technologies, tools, and methods
- Advanced programming skills in Python and PySpark
- Experience with machine learning platforms like SageMaker, MLflow, or similar
- Expertise in machine learning libraries and tools (e.g., PyTorch, Scikit-learn, etc.)
Preferred Qualifications
- Experience in a fast-paced, entrepreneurial environment
- Proven track record of peer-reviewed publications
- Experience working with clinical and genomic data
- Familiarity with cloud services such as AWS
- Experience developing machine learning models for cardiovascular applications
- Exceptional analytical and problem-solving skills, with a strong emphasis on multi-modal medical datasets
