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.
Qualifications
- 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.
- 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.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This 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.
Skills Required
- Master’s degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field and 3+ years of technical engineering experience; or bachelor’s degree in a related field and 5+ years of 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
- Ability to meet Microsoft, customer, and/or government security screening requirements
- 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 112G/224G SerDes, PAM4, MAC/PCS, FEC, link training, signal integrity, DACs, or AECs; or optical networking expertise with OSFP/QSFP-DD, AOCs, optical transceivers, link characterization, and diagnostics
- Familiarity with 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 data center infrastructure and hardware development from prototype through production
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
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Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
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Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.
Microsoft Insights
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