Microsoft
Member of Technical Staff - Software Engineer, Health AI
New York, NY, US · Staff+
Sponsorship not specifiedDetected 15 hours ago
JavaScriptPythonJavaC++C#Distributed SystemsCloud PlatformsLLMsCommunication
About the role
- Work independently across a wide range of our stack, shipping delightful user experiences.
- Ensure resilience, maintainability, and security above all else.
- Guide peers, contributing to a culture of technical excellence and continuous improvement.
Responsibilities
- Own the end-to-end development of features, from ideation and specification through to deployment and iteration.
- Design, build, and optimize production-grade code, delivering robust features within a much larger existing architecture.
- Build the hiring pipelines, onboarding frameworks, or software development best practices as needed to scale an engineering team around you.
- Demonstrated expertise building products at scale, with domain expertise in one or more of distributed systems, cloud infrastructure, web, mobile, GenAI.
- Experience collaborating in cross functional teams, working through ambiguity to deliver high quality products.
- Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.
- Experience developing and improving evaluation methodologies for assessing quality of LLM-based products.
Benefits
- Collaborate with AI researchers, product managers, and designers to bring a world-class AI health companion to the world.
- Have 0 to 1 experience with a bias towards shipping and learning, while balancing a high-quality bar.
- Experience in healthcare technology, particularly with regulated medical devices.
- Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies and patterns in AI.
Apply directly at Microsoft →Create a free account for alerts like thisView Microsoft immigration profile
This listing is sourced directly from Microsoft's careers page and normalized into a canonical job model.