Scientific Software Engineer with AI Concentration

3 weeks ago

Baltimore, Maryland, United States Johns Hopkins University Full time
Job Summary

The Johns Hopkins Center for Data Science in Emergency Medicine is seeking a highly skilled Scientific Software Engineer with a concentration in AI to join our team. As a key member of our research software engineering team, you will contribute to the advancement of scientific software and the creation of systems based on novel combinations of human and artificial intelligence and advanced data science methods to improve patient care.

This role utilizes the strengths of scientific computing to drive innovation in high-value target projects identified across all areas of emergency medicine and particularly clinical decision support.

Key Responsibilities:

  • Collaborate on ground-breaking research projects and clinical decision support tools that demand advanced software solutions.
  • Design and build innovative AI solutions for CDEM projects in collaboration with software developers, data scientists, clinicians, and scientific subject matter experts and users.
  • Utilize XGBoost, random forest, and the latest deep neural network libraries trained on state-of-the-art hardware to predict outcomes and forecast time series data.
  • Work with clinicians and data scientists to analyze massive patient-level data sets and develop software pipelines for real-time high-frequency data processing workflows.
  • Work closely with internal and external stakeholders to understand interface and backend server needs, envision solutions, and develop prototypes and functional requirements.

Requirements:

  • Strong scientific literacy and curiosity, ability to continually adapt to new scientific domains and problems and identify and articulate scientifically informed AI and data analysis approaches.
  • Strong experience (2+ years) in software development, with a focus on building robust, scalable, and maintainable software solutions.
  • Prior experience specifically in developing machine learning models, preferably within the healthcare or biomedical domain.
  • Proficiency in Python programming language, as well as experience with relevant libraries and frameworks like TensorFlow, PyTorch, scikit-learn, and Keras.
  • Knowledge of Machine Learning and Clinical Decision Support, including solid understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, and ensemble methods.
  • Proficiency in software development best practices, including version control (e.g., Git), unit testing, continuous integration/continuous deployment (CI/CD), and agile development methodologies.
  • Experience with cloud computing platforms (e.g., AWS, Google Cloud Platform, Azure) and containerization technologies (e.g., Docker, Kubernetes) for deploying and scaling machine learning models.
  • Strong problem-solving skills and the ability to translate research findings into production-ready software solutions.
  • Excellent communication skills, both verbal and written, with the ability to effectively communicate complex technical concepts to non-technical stakeholders.
  • Experience collaborating with interdisciplinary teams, including clinicians, data scientists, and other software engineers, to define requirements, develop solutions, and validate model performance.
  • Awareness of regulatory requirements governing healthcare software development, such as HIPAA and FDA guidelines for medical devices and software.
  • Understanding of ethical considerations and privacy concerns related to handling sensitive patient data, and a commitment to maintaining data security and confidentiality.

Preferred Qualifications:

  • Master's Degree or PhD in a quantitative discipline.
  • Seven or more years of experience in the above areas.
  • Experience designing and developing advanced scientific data visualizations.
  • Knowledge of healthcare data standards, such as HL7, DICOM, FHIR, and experience working with electronic health records (EHR) data.
  • Familiarity with clinical decision support systems, including their design, implementation, and integration into healthcare workflows.

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