About the Role
Job Summary: We are seeking a talented Software Engineer to join our dynamic team and contribute to the development of innovative software solutions. You will be responsible for designing, coding, testing, and maintaining software applications that integrate with our advanced MRI technologies. The ideal candidate should have strong problem-solving skills, a passion for developing cutting-edge technology, and the ability to work collaboratively in a fast-paced environment.
Key Responsibilities:
Design, develop, and optimize software solutions for medical imaging systems.
Collaborate with cross-functional teams to implement software features that enhance MRI diagnostics.
Debug, troubleshoot, and resolve technical issues in software applications.
Ensure the software meets industry standards for quality, performance, and security.
Write and maintain technical documentation for software systems.
Participate in code reviews to ensure adherence to best practices.
Requirements
Qualifications:
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
Proficiency in programming languages such as C++, Python, or Java.
Experience with software development frameworks and version control systems (e.g., Git).
Strong understanding of software development life cycle (SDLC).
Familiarity with medical imaging technologies and protocols (e.g., DICOM) is a plus.
Excellent analytical and problem-solving skills.
Preferred Qualifications:
Experience in healthcare or MedTech industries.
Knowledge of AI/ML integration in software development.
Familiarity with MRI systems and data processing.
About the Company
BioProtonics is a pioneering company focused on advancing diagnostic imaging through its Magnetic Resonance Histopathology (MRH) technology. MRH offers a non-invasive, high-resolution method for analyzing tissue microarchitecture during routine MRI scans. By capturing detailed spectral data across various wavelengths, MRH provides valuable insights into tissue health, helping differentiate between normal and potentially diseased or cancerous tissues. This innovative approach aims to enhance early disease detection, reduce the need for invasive biopsies, and improve patient care through more precise diagnostic capabilities.