Senior AI Network Architect Job at Microsoft Corporation (Redmond)

Job Description

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

Job Responsibility

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 across compute, storage, and interconnect domains to support scalable AI training workloads
Partner with silicon, firmware, and datacenter engineering teams to co-design infrastructure that meets performance, reliability, and deployment goals
Influence platform decisions across rack, chassis, and pod-level implementations
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
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
Shape the direction of hyperscale AI infrastructure by engaging with standards bodies (e.g., IEEE 802.3), influencing component roadmaps, and driving adoption of novel interconnect protocols and topologies

Requirements

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
Ability to meet Microsoft, customer and/or government security screening requirements
Microsoft Cloud Background Check
3+ 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 training and Inference workloads, 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, and cabled backplanes
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

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Senior AI Network Architect

Job Description

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