Software Engineer developing and maintaining IoT gateway solutions at a dynamic startup expanding in Japan. Collaborating on system design and addressing complex issues.
About the role
- Leading software engineering initiatives for Precision Neuroscience's medical device software. Overseeing R&D and commercial deployment of cutting-edge neural data systems.
Responsibilities
- Oversee all Software Engineering projects and initiatives, ensuring our products are safe, effective, and secure, and delivered on time and on budget
- Define the overall software development practices that support high-quality and timely delivery of Precision’s software roadmap
- Successfully partner across the organization, particularly with device hardware and ML R&D teams, to achieve overall company objectives
- Review technical designs and sign-off on development plans for key software initiatives, especially those involving device integration and the implementation of ML algorithms
- Communicate effectively to both technical and non-technical audiences to ensure alignment with software team goals, milestones, and risks
- Build and grow a high-performing software organization through hiring, developing people, and creating a strong team culture that retains key talent
Requirements
- 15+ years’ experience in software development in a medical device environment (preferably Class III), including 10+ years’ experience as a director or above
- Bachelor’s degree or above in computer science, ECE or related field
- Proven track record of building and scaling high-performing teams supporting full life-cycle software development through commercialization
- Experience in developing a scalable systems architecture that supports cross-platform software development including desktop, mobile and cloud environments
- Experience interfacing with embedded systems teams and supporting software-hardware integration
- Knowledge and application of medical device software development standards, such as IEC 62304, and FDA guidance on software development, usability and cybersecurity
- Successful track-record of working in fast-paced and interdisciplinary engineering teams
- Strong experience collaborating with Quality and Regulatory working groups preferred
- Familiarity with modern machine learning concepts and collaboration with ML R&D teams is a plus
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