Principal AI Network Architect

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 deliveringa trustedexperience to customers and partnersworldwideand we are looking for passionateengineers to help achieve that mission.

As Microsoft's cloud business continues togrowthe ability to deploy new offerings and hardware infrastructure on time, inhigh volume with high quality and lowest cost is of paramount importance. To achieve this goal,theCloudHardwareSystems Engineering (CHSE)team is instrumental in defining and delivering operational measures of success for hardware manufacturing, improving the planning process, quality, delivery,scaleand sustainability related to Microsoft cloud hardware. We are looking for seasoned engineers with a dedicated passion for customer focused solutions,insightand industry knowledge to envision and implement future technical solutions that will manage andoptimizethe Cloud infrastructure.

We are looking foraPrincipalAI Network Architectto join the team.

#SCHIE#azurehwjobs

• Technology Leadership
  Spearhead architectural definition and innovation for next-generation GPU and AI accelerator platforms, with a focus on ultra-high bandwidth, low-latency backend networks. Drive system-level integration acrosscompute, storage, and interconnect domains to support scalable AI training workloads.

• Cross-Functional Collaboration
  Partner with silicon, firmware, and datacenter engineering teams to co-design infrastructure thatmeets performance, reliability, and deployment goals. Influence platform decisions across rack, chassis, and pod-level implementations.

• Technology Partnerships
  Cultivate deep technical relationships with silicon vendors, optics suppliers, and switch fabric providers to co-develop differentiated solutions. Represent Microsoft in joint architecture forums and technical workshops.

• Architectural Clarity
  Evaluate and articulate tradeoffs across electrical, mechanical, thermal, and signal integrity domains. Frame decisions in terms of TCO, performance, scalability, and deployment risk. Lead design reviews and contribute to PRDs and system specifications.

• Industry Influence
  Shape the direction of hyperscale AI infrastructure by engaging with standards bodies (e.g., IEEE 802.3), influencingcomponentroadmaps, and driving adoption of novel interconnect protocols and topologies.

Required Qualifications:

• Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience
  • OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience
  • OR equivalent experience.

Other Requirements:

Abilityto meet Microsoft,customerand/or government security screening requirements arerequiredfor 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:

• 5+ years of experience in designing AI backend networks and integrating them into large-scale GPU systems.
• Proven expertise in system architecture across compute, networking, and accelerator domains.
• Deep understanding of RDMA protocols (RoCE, InfiniBand), congestion control (DCQCN), and Layer 2/3 routing.
• Experience with optical interconnects (e.g., PSM, WDM), link budget analysis, and transceiver integration.
• Familiarity with signal integrity modeling, link training, and physical layer optimization.
• Experience architecting backend networks for AI trainingand Inferenceworkloads, including Hamiltonian cycle traffic and collective operations (e.g., all-reduce, all-gather).
• Hands-on design of high-radix switches (400Gbps per port), orthogonal chassis, andcabledbackplanes.
• Knowledge of chip-to-chip and chip-to-module interfaces, including error correction and equalization techniques.
• Experience with custom NIC IPs and transport layers for secure, reliable packet delivery.
• Familiarity with AI model execution pipelines and their impact on pod-level network design and latency SLAs.
• Prior contributions to hyperscale deployments or cloud-scale AI infrastructure programs.