Machine Learning Engineer

2 months ago

Chicago, United States Cardio Diagnostics Inc. Full time

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

 

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