Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) is the team behind Microsoft’s expanding Cloud Infrastructure and responsible for powering Microsoft’s “Intelligent Cloud” mission. SCHIE delivers the core infrastructure and foundational technologies for Microsoft’s over 200 online businesses including Bing, MSN, Office 365, Xbox Live, Teams, OneDrive, and the Microsoft Azure platform globally with our server and data center infrastructure, security and compliance, operations, globalization, and manageability solutions. Our focus is on smart growth, high efficiency, and delivering a trusted experience to customers and partners worldwide and we are looking for passionate engineers to help achieve that mission.

Microsoft’s Hardware Systems organization is developing AI-native silicon and system-level solutions to power the next generation of frontier AI models. The MAIA platform combines custom accelerators, advanced networking technologies, large-scale distributed infrastructure, and cloud-scale software to deliver industry-leading AI training and inference capabilities.

The Platform Systems Engineering (PSE) team is seeking a Sr. AI Network Systems Engineer to develop, integrate, validate, and deploy networking solutions for next-generation MAIA AI systems.

This role works at the intersection of networking hardware, custom silicon, AI infrastructure, and hyperscale systems. You will collaborate across architecture, silicon, firmware, hardware, software, validation, manufacturing, and Azure infrastructure teams to deliver high-performance scale-up and scale-out networking for AI training and inference platforms. The role spans switches, NICs, PHYs, MAC/PCS, high-speed SerDes, copper and optical interconnects, and rack-scale networking infrastructure.

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.

Responsibilities

AI Networking Architecture & System Development

• Develop and integrate scale-up and scale-out networking solutions for MAIA AI training and inference systems.

• Translate AI/ML workload requirements into system-level networking requirements.

• Evaluate architecture tradeoffs across performance, reliability, power, scalability, serviceability, and cost.

Network Hardware Integration & Bring-Up

• Drive integration, bring-up, and qualification of switches, NICs, PHYs, MAC/PCS, SerDes, optics, cables, and backplane technologies.

• Collaborate across silicon, firmware, hardware, software, and OS teams to resolve complex integration issues.

• Partner with technology vendors, ODMs, and CMs through qualification and production readiness.

AI Fabric Validation & Performance

• Develop end-to-end validation strategies covering functionality, performance, scale, interoperability, reliability, and stress.

• Characterize AI fabric performance including bandwidth, latency, link utilization, congestion behavior, and communication efficiency.

• Develop validation methodologies for rack-scale and cluster-scale AI infrastructure.

High-Speed Electrical & Optical Interconnects

• Drive qualification of next-generation electrical and optical networking technologies, including high-speed SerDes, copper interconnects, optical modules, and high-density connectivity.

• Evaluate technologies such as PAM4 SerDes, DAC/AEC, OSFP/QSFP-DD, advanced optical interconnects, LPO/LRO, CPO, and silicon photonics.

• Analyze system-level tradeoffs across performance, power, reliability, signal/link integrity, and scalability.

Debug, Telemetry & Automation

• Lead root-cause analysis of complex networking issues across PHY, SerDes, MAC/PCS, switches, NICs, firmware, and system software.

• Develop and improve telemetry, diagnostics, validation automation, and network health monitoring.

• Use lab characterization and system telemetry to drive issues to resolution and improve platform reliability.

• Master’s Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience
• OR Bachelor’s Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience
• OR equivalent experience.
• 5+ years of experience developing or validating networking, compute, storage, or accelerator hardware systems.
• 5+ years of experience designing, integrating, validating, or troubleshooting Ethernet-based networking infrastructure, including switches, NICs, optics, PHYs, or high-speed SerDes technologies.
• 5+ years of experience supporting AI, HPC, cloud, or large-scale data center infrastructure deployments.

Other Requirements:

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 GPU, FPGA, TPU, or custom AI accelerator systems supporting AI/ML workloads.

• Experience with scale-up and scale-out AI networking and distributed AI infrastructure.

• Electrical networking expertise in areas such as 112G/224G SerDes, PAM4, MAC/PCS, FEC, link training, signal integrity, DACs, and AECs, or optical expertise in areas such as OSFP/QSFP-DD, AOCs, optical transceivers, optical link characterization, and diagnostics.

• Familiarity with optical technologies such as DR4/DR8/FR4, optical link budgets, OMA, TDECQ, receiver sensitivity, LPO, LRO, CPO, or silicon photonics.

• Understanding of AI workload communication patterns, network collectives, traffic profiles, and congestion behavior.

• Familiarity with IEEE Ethernet, OIF, CMIS, and emerging AI networking technologies.

• Experience with Linux, telemetry, diagnostics, scripting, test automation, and network qualification.

• Experience with hyperscale datacenter infrastructure and hardware development from prototype through production.

azure #MAIA #AI/ML #Networking Hardware

Hardware Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 per year.