Principal AI Network Hardware Systems Engineer at Microsoft – United States | Jobyssey

Overview

Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) powers the infrastructure behind Microsoft's Intelligent Cloud, delivering the foundational technologies that support services including Azure, Microsoft 365, Teams, Bing, Xbox Live, and more. As Microsoft continues to advance AI innovation, SCHIE is developing AI-native silicon and system-level solutions that enable next-generation AI training and inference at hyperscale.

The Platform Systems Engineering (PSE) team is seeking a Principal AI Network Hardware Systems Engineer to lead the architecture, bring-up, validation, optimization, and deployment of networking infrastructure for Microsoft's MAIA AI platform. This role combines networking hardware, systems architecture, AI infrastructure, and large-scale deployment to deliver industry-leading AI performance and reliability.

You will work across the networking stack, spanning high-speed SerDes, optics, cables, NICs, PHYs, switch silicon, AI communication frameworks, and distributed training systems. As a Principal engineer, you will influence architectural direction, guide technical strategy, and collaborate across silicon, firmware, hardware, software, validation, manufacturing, and Azure engineering teams.

This is a unique opportunity to shape the future of AI networking infrastructure and drive technologies that power Microsoft's next generation of hyperscale AI systems.

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 issues impacting latency, throughput, scalability, and reliability.

Drive optimization of network communication paths supporting distributed AI training and inference.

RDMA & AI Fabric Technologies

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.

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, bandwidth utilization, 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.

Qualifications

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
8+ years of experience in NW HW development
8+ years of experience in GPU based SU/SO development
8+ years of hands on experience with HS interface architecture and development

Other Qualifications:

Ability to meet Microsoft, customer and/or government security screening requirements are required for 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 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).

#azure #MAIA #AI/ML #Networking Hardware

Hardware Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 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 $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

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.

Principal AI Network Hardware Systems Engineer

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