Senior AI Network Systems Engineer
About The Position
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. The Platform Systems Engineering (PSE) team is seeking a Senior AI Network Systems Engineer to 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-generation AI systems 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.
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.
- 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.
- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.
Nice To Haves
- Experience with RDMA technologies, AI fabrics, and distributed training environments.
- Understanding of RoCE, congestion control, ECN, PFC, DCQCN, and related AI networking technologies.
- Experience with AI/ML workload communication patterns and collective operations.
- Experience with SONiC, 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).
Responsibilities
- 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.
- 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 issues impacting latency, throughput, scalability, and reliability.
- Drive optimization of network communication paths supporting distributed AI training and inference.
- Design, validate, and optimize RDMA-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 to optimize AI fabric performance and workload scalability.
- Develop validation methodologies for AI traffic patterns and collective communication workloads.
- 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, bandwidth utilization, congestion events, flow distribution, and workload communication patterns.
- Create and automate network stress, scale, and performance qualification methodologies.
- 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.
- 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.
Benefits
- Certain roles may be eligible for benefits and other compensation.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
