Principal AI Network Hardware Systems Engineer
Microsoft · Washington, United States +1 · 18h ago
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
- 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
- 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:
https://careers.microsoft.com/us/en/us-corporate-pay
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.