engineering
Posted May 5Staff Product Engineer
at ResMed
San Diego United States, United StatesOn-site
Responsibilities
- Build intuitive, high-quality user interfaces aligned with enterprise UX standards.
- Ship iteratively while maintaining enterprise-grade reliability, security, and compliance.
- Collaborate Across Value Streams Partner closely with Product and Design from problem definition through delivery.
- Facilitate clear technical conversations across engineering teams.
- Integrate cleanly with internal platforms and external systems.
- Improve shared patterns where appropriate without reinventing foundational infrastructure.
- Promote outcome-driven, product-centric thinking.
- Foster a culture of collaboration, shared ownership, and continuous improvement.
Requirements
- Your ability to build trust, align stakeholders, and communicate clearly will be as important as your ability to architect and ship durable systems.
- Leveraging AI-native development practices and modern engineering tooling, you will create outsized impact by moving quickly on strong foundations.
- This role models the future of engineering within the organization: collaborative, product-centric, AI-accelerated, and enterprise-aligned — while remaining deeply hands-on in system design and software development.
- Model Native-AI Engineering Use advanced AI coding tools (e.g., Claude Code, Augment Code, Copilot) as disciplined force multipliers.
- Critically evaluate AI-generated output for correctness, performance, scalability, and security.
- Help define responsible, high-quality AI-assisted development practices.
- Deep backend expertise (e.g., Java/Spring Boot or equivalent).
- Experience with event-driven and asynchronous architectures.
- Experience operating within enterprise architecture and DevX standards.
- Advanced proficiency using AI-assisted development tooling in daily practice.
- AI-native practitioners who combine speed with discipline.
- Why This Role Matters This role will help define how modern product engineering is practiced within the organization — combining enterprise stability with startup-level velocity and AI-native execution to deliver scalable, meaningful outcomes.