Senior AI Cloud Optical Network Engineer - Hardware

Have you noticed that a new records of optical fiber loss for communication is achieved and improving with Hollow Core Fiber (HCF)? Do you want to shape the future of Artificial Intelligence (AI) with cutting-edge optical networking technologies? Do you imagine connecting many Graphics Processing Units (GPUs) and Central Processing Units (CPUs) with high-speed optical links to build the next level of computing power for every person and every organization in the world? If so, this role is ideal for you.

As a Senior AI Cloud Optical Network Engineer - Hardware you will get to design and implement optical networking systems for AI and for HCF, work on global network strategy, lead the optical networking technology innovation, and create auto control and management platforms to make the network accessible, reliable, and self-healing. This role gives you the opportunity to advance your career in a thriving area of technological innovation, acquire deep knowledge in optical networking tailored for AI applications, and learn the skills required for large-scale network automation. You’ll enjoy a flexible work environment where you can work from home partly and come to the office/lab when needed. Join us in making an impact and expanding the limits of what's possible.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

• Optical Network Design for Innovative Systems: Collaborate with key stakeholders to ascertain customer requirements specific to optical networking for AI and for HCF systems. Lead the design and development of specialized optical network components, focusing on optical direct-detection and coherent technologies, to meet both customer and technical requirements. Create comprehensive design documents that detail the architectural blueprint for optical solutions optimized for AI applications.
• Testing and Validation: Conduct rigorous testing and validation of optical systems and components. Utilize automation tools to establish test cases, and identify gaps in test coverage, ensuring the transceivers' high-performance compatibility with AI applications.
• Code Implementation and Optimization: Develop, optimize, debug, and refactor code tailored for the management and automation of optical networks, focusing on scalability and efficiency in AI-driven network environments.
• Project and Feature Leadership: Utilize your in-depth expertise in coherent and direct-detection optical technologies to steer project and release plans in coordination with project managers and stakeholders. Make sure these plans align with the overarching objectives of optical networking optimized for AI systems.
• Operational Readiness and Incident Management: Act as the Designated Responsible Individual (DRI) during on-call rotations, efficiently responding to incidents with significant customer or business impact. Assess the level of impact, troubleshoot issues, and deploy targeted fixes to resolve root causes. Coordinate with multiple teams, from product owners to engineers, to facilitate timely incident resolution. Implement automation strategies to prevent recurrence of issues. Escalate complex or ambiguous problems as needed, and contribute to postmortem reviews, sharing insights on incidents and their resolutions.
• Telemetry and Data Analysis for Optical Networks: Monitor network telemetry designed specifically for AI-optimized optical networks. Leverage data analytics to detect patterns, errors, and unexpected issues, using these insights to make informed decisions on product and service improvements.
• Continuous Improvement and Trend Adaptation: Stay abreast of the latest trends and technologies in optical networking and AI. Seek out new knowledge and techniques that can improve the availability, reliability, efficiency, and scalability of AI-optimized optical networks, adapting new solutions and best practices accordingly.