Principal AI Network Hardware Systems Engineer

Microsoft Silicon Cloud Hardware and Infrastructure Engineering (SCHIE) powers the infrastructure behind Microsofts Intelligent Cloud delivering the foundational technologies that support services including Azure Microsoft 365 Teams Bing Xbox Live and more. As Microsoft continues to advance AI innovation SCHIE is developing AI-native silicon and system-level solutions that enable next-generation AI training and inference at hyperscale.

The Platform Systems Engineering (PSE) team is seeking a Principal AI Network Hardware Systems Engineer to lead the architecture bring-up validation optimization and deployment of networking infrastructure for Microsofts MAIA AI platform. This role combines networking hardware systems architecture AI infrastructure and large-scale deployment to deliver industry-leading AI performance and reliability.

You will work across the networking stack spanning high-speed SerDes optics cables NICs PHYs switch silicon AI communication frameworks and distributed training systems. As a Principal engineer you will influence architectural direction guide technical strategy and collaborate across silicon firmware hardware software validation manufacturing and Azure engineering teams.

This is a unique opportunity to shape the future of AI networking infrastructure and drive technologies that power Microsofts next generation of hyperscale AI systems.

Microsofts 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.

AI Network Architecture & System Integration

• Define and develop networking requirements for large-scale AI training and inference clusters.

• Collaborate with silicon system software firmware hardware and Azure infrastructure teams to deliver scalable networking solutions from concept through datacenter deployment.

• Participate in architecture reviews and influence next-generation AI networking roadmaps.

• Define network concepts of operation serviceability requirements telemetry requirements and operational models for AI infrastructure.

Layer 3 / Layer 4 Networking

• Lead design and validation of IP-based AI networking solutions spanning TCP/IP UDP routing congestion management flow control QoS and traffic engineering.

• Analyze transport-layer behavior and performance characteristics across large-scale distributed AI workloads.

• Evaluate network protocol implementations and debug issuesimpactinglatency throughput scalability and reliability.

• Drive optimization of network communication paths supporting distributed AI training and inference.

RDMA & AI Fabric Technologies

• Designvalidate andoptimizeRDMA-based networking solutions for AI clusters.

• Analyze RDMA performance congestion behavior packet loss retransmissions and collective communication efficiency.

• Work closely with networking vendors and software teams tooptimizeAI fabric performance and workload scalability.

• Develop validation methodologies for AI traffic patterns and collective communication workloads.

Performance Characterization & Validation

• Develop and execute networking validation strategies covering functionality performance scale interoperability resiliency and reliability.

• Characterize network behavior under AI training and inference workloads.

• Evaluate latency bandwidthutilization congestion events flow distribution and workload communication patterns.

• Create and automate network stress scale and performance qualification methodologies.

Debugging & Root Cause Analysis

• Lead end-to-end troubleshooting of networking issues across physical data link network and transport layers.

• Perform packet-level analysis and protocol debugging using telemetry packet captures performance counters and diagnostic tools.

• Investigate network switch NIC RDMA routing congestion control and protocol-related issues.

• Drive corrective actions and long-term reliability improvements using fleet telemetry and lab validation.

Automation & Observability

• Build and improve network observability diagnostics telemetry and monitoring solutions.

• Develop tools and automation for network validation performance analysis and failure detection.

• Improve engineering productivity through automated testing qualification and network health assessment frameworks.

Required Qualifications:

• Masters Degree in Electrical Engineering Computer Engineering Mechanical Engineering or related field AND 7 years technical engineering experience
  • OR Bachelors Degree in Electrical Engineering Computer Engineering Mechanical Engineering or related field AND 8 years technical engineering experience
  • OR equivalent experience
• 8 years of experience in NW HW development
• 8 years of experience in GPU based SU/SO development
• 8 years of hands on experience with HS interface architecture and development

Other Qualifications:

Ability to meet Microsoft customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• Experience with RDMA technologies AI fabrics and distributed training environments.
• Understanding of RoCE congestion control ECN PFC DCQCN and related AI networking technologies.
• Experience with AI/ML workload communication patterns and collective operations.
• Experience with SONiC Linux networking networking telemetry and network operating systems.
• Experience with network switches SmartNICs DPUs NIC offloads and large-scale cloud infrastructure.
• Familiarity with AI networking technologies including Ultra Ethernet and hyperscale AI cluster architectures.
• Experience developing network stress tools validation frameworks performance benchmarks or observability solutions.
• Knowledge of packet analysis tools telemetry infrastructure and network automation frameworks.
• Exposure to high-speed networking environments (200G/400G/800G Ethernet).

#azure#MAIA#AI/ML#Networking Hardware

Hardware Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142800 - $274800 per year. There is a different range applicable to specific work locations within the San Francisco Bay area and New York City metropolitan area and the base pay range for this role in those locations is USD $188000 - $304200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
position will be open for a minimum of 5 days with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age ancestry citizenship color family or medical care leave gender identity or expression genetic information immigration status marital status medical condition national origin physical or mental disability political affiliation protected veteran or military status race ethnicity religion sex (including pregnancy) sexual orientation or any other characteristic protected by applicable local laws regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process read more about requesting accommodations.

Required Experience:

IC