- San Francisco, United States
- Erin Crider
- about 1 month ago
Our mission is to make medical imaging universally accessible. We have assembled a team of domain experts in machine learning, visual neuroscience, medical devices, cardiologists, clinicians, engineers, and scientists who are developing breakthrough technologies in cardiovascular imaging and care addressing the largest cause of death in the United States.
Our team brings diverse expertise to the problem of diagnosing and managing heart disease with leaders in machine learning, visual neuroscience, robotics, and physics. We have teamed up with a network of world-class clinical and academic advisors, progressing in a very short period of time.
As a Sr. Application Developer you will be a core member of the Engineering team with the opportunity to take on a variety of projects in the cloud infrastructure, data pipeline, full stack product development, and deployment areas. The specific projects you will own will depend on your combination of expertise and interests.
What we are looking for:
- 4-5 years experience working in a production environment (post academia)
- BS degree in Computer Science or related technical field, or equivalent practical experience required
- Extensive knowledge of Python
- Experience in designing and developing applications end-to-end
- Knowledgeable on code reviews and software engineering best practices
- Experience with Google Cloud, AWS or Azure
- Strong teamwork ethic, passion for learning and desire to seek new challenges
- Bonus: Familiarity with Python scientific computing packages (numpy, scipy, pandas, scikit-image, scikit-learn, etc)
- Bonus: Experience working on Machine Learning applications
- Bonus: Experience with the DICOM format or other medical imaging tools
- Design, build and maintain APIs, services, and systems
- Develop a high quality, customer facing software product
- Architect large system application that integrates with machine learning microservices
- Conduct design and code reviews
- Analyze and improve the efficiency and scalability of our systems and processes
- Collaborate with multiple teams
- Demonstrate and uphold high engineering standards and bring consistency/improvements to our codebases and processes