Senior AI Network Systems Engineer

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 partnersworldwideand we are looking for passionateengineers to help achieve that mission.

The Platform Systems Engineering (PSE) team is seeking aSenior AI Network Systems Engineerto drive the architecture, integration, validation, optimization, and deployment of large-scale AI networking infrastructure. This role focuses on Layer 3 and Layer 4 networking technologies, RDMA-based fabrics, TCP/UDP transport behavior, network performance, congestion management, and scale-out AI networking environments.

You will work closely with networking, silicon, firmware, system software, validation, and Azure infrastructure teams to build reliable, high-performance AI networks that support next-generationAIsystems deployed at hyperscale.

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 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

• Design,validate, 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.

• 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 for accelerator based systems.
• 5+ years of experience designing, integrating, validating, or troubleshooting Ethernet-based networking infrastructure, including Network switches.
• 5+ years of experience supporting AI, HPC, cloud, or large-scale data center infrastructure deployments.

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

• Experience with RDMA technologies, AI fabrics, and distributed training environments.

• Understanding ofRoCE, congestion control, ECN, PFC, DCQCN, and related AI networking technologies.

• Experience with AI/ML workload communication patterns and collective operations.

• Experience withSONiC, 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 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.

Microsoft is a global technology company headquartered in Redmond, Washington. Our mission is to empower every person and every organization on the planet to achieve more. We develop, license, and support a wide range of software products, services, and devices that help individuals and businesses realize their full potential.

Our flagship products include the Microsoft 365 productivity cloud, Windows operating system, Azure cloud platform, and Dynamics 365 business applications. We are also a leader in areas such as artificial intelligence, cybersecurity, developer tools, and gaming through Xbox and Game Pass.

With operations in more than 190 countries and over 220,000 employees worldwide, Microsoft is committed to responsible innovation, inclusive economic growth, and sustainability. We work closely with governments, industries, and communities to ensure that technology serves the public good and helps address some of the world’s most pressing challenges.

As we celebrate our 50th anniversary in 2025, we continue to look forward—investing in AI, cloud, and quantum computing to shape the future of work, education, and society at large scale.