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

Cardio Diagnostics Inc.

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Machine Learning Engineer - Cardiovascular Epigenetics & Genetics