Principal AI Back-End Network Systems Lead

Overview

Microsoft Silicon, Cloud Hardware, and Infrastructure Engineering (SCHIE) is the team behind Microsofts expanding Cloud Infrastructure and is responsible for powering Microsofts Intelligent Cloud mission. SCHIE delivers the core infrastructure and foundational technologies that support Microsoft Azure and other online services globally. Our focus is on smart growth, high efficiency, reliability, and a trusted experience for customers and partners worldwide.

The Platform Systems Engineering (PSE) team is seeking a Principal AI Back-End Network Systems Lead to serve as the technical lead and end-to-end product development owner for Back End Network across both scale-up and scale-out. The role will lead the product from requirements and architecture through design, integration, validation, manufacturing readiness, deployment, and fleet learning - spanning silicon, network adapters, switches, interconnects, optics/electrical channels, and the fabric as a complete system.

The successful candidate will bring deep expertise in one or more AI/ML scale-up or scale-out networking domains and the product-level thinking to connect decisions across the full system. This lead will work from physical and data-link technologies through network and transport behavior to collectives, workloads, modeling, observability, and datacenter deployment, creating clarity across architecture, execution, quality, performance, reliability, power, cost, and schedule.

#azure #MAIA #AI/ML #Networking #ScaleUp #ScaleOut

Responsibilities

End-to-End Product Ownership & Architecture

• Own the Back End Network product definition and execution plan across scale-up and scale-out, from customer/workload needs and product requirements through architecture, development, qualification, deployment, and fleet learning.
• Translate AI training and inference requirements into measurable system targets for bandwidth, latency, scale, collective efficiency, reliability, availability, serviceability, power, thermal behavior, cost, security, and schedule.
• Make and drive cross-system architecture decisions, interface contracts, feature priorities, and risk tradeoffs; maintain clear ownership, milestones, entry/exit criteria, and product readiness metrics.
• Lead NUDD (new, unique, different, and difficult) technology assessments, prototypes, make/buy/partner decisions, and mitigation plans for the highest product risks.

Scale-Up/Scale-Out Systems, Silicon, Switch & Fabric

• Lead architecture and integration across accelerator and networking silicon, NICs/DPUs, PHYs/retimers, switch ASICs, switches, cables/backplanes, optics, and fabric topology.
• Define scalable scale-up and scale-out architectures, including topology, radix, reach, link speeds, routing, path diversity, oversubscription, failure domains, and resiliency.
• Drive silicon, board, switch, firmware, network operating system, and fabric requirements so that components operate as one high-performance and serviceable product.
• Partner with silicon, switch, optical, cable, connector, system, manufacturing, and supplier teams to deliver designs from concept through production and datacenter integration.

Optical/Electrical & Cross-Layer Integration

• Oversee electrical and optical technologies for high-speed links, including signal integrity, SerDes/PHY, link training, FEC, reach, power, thermal, mechanical, manufacturability, and interoperability tradeoffs.
• Drive end-to-end behavior across the relevant OSI layers - physical, data link, network, and transport - and the system/runtime interfaces above them.
• Define and validate Ethernet/RDMA or other scale-up/scale-out protocol behavior, including flow control, congestion control, QoS, routing, packet loss/recovery, telemetry, and security.
• Ensure hardware, firmware, drivers, network operating systems, management software, orchestration, and datacenter operations are aligned to common specifications and concepts of operation.

Collectives, Workloads, Modeling & Performance

• Connect network architecture to distributed AI workloads, parallelism strategies, traffic patterns, and collective operations such as all-reduce, all-gather, reduce-scatter, and all-to-all.
• Establish analytical and simulation models for topology, bandwidth, latency, congestion, failure behavior, collective efficiency, workload scaling, power, and cost.
• Use models, emulation, prototypes, and lab/fleet data to predict bottlenecks, guide design choices, set performance budgets, and validate architectural assumptions.
• Partner with workload, collective-communication, compiler/runtime, and application teams to co-optimize the network and improve job completion time and cluster efficiency.

Validation, Reliability, Deployment & Operations

• Own the end-to-end validation strategy covering functionality, interoperability, performance, scale, stress, resiliency, reliability, security, serviceability, and upgrade/recovery behavior.
• Lead bring-up and root-cause analysis across silicon, electrical/optical links, switch, fabric, protocol, system software, collective, and workload layers using telemetry, packet captures, counters, diagnostics, and lab characterization.
• Define production qualification, manufacturing test, deployment, monitoring, rollback, and fleet-health requirements; use operational learning to drive corrective actions and next-generation improvements.
• Drive product readiness reviews and ensure the solution can be safely and repeatably deployed, operated, serviced, and scaled in hyperscale datacenters.

Technical Leadership & Ecosystem Execution

• Create clarity and technical alignment across architecture, silicon, hardware, optics, firmware, networking, software, validation, manufacturing, datacenter, and workload teams.
• Influence internal roadmaps and external partners, including silicon, switch, optical, interconnect, ODM/OEM, and open-source ecosystems; resolve cross-team gaps and ambiguous ownership.
• Communicate product status, architectural decisions, risks, tradeoffs, and executive asks clearly while mentoring engineers and raising the quality of end-to-end systems engineering.

Qualifications

Required Qualifications

• Masters Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 7+ years technical engineering experience
• OR Bachelors Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 8+ years technical engineering experience
• OR equivalent experience

Other Requirements:

• 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:

• Bachelors degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field and 10+ years of relevant experience in networking, distributed systems, accelerator platforms, or cloud infrastructure

• or equivalent experience.

• 6+ years of experience designing, developing, integrating, or validating networking for AI/ML, HPC, accelerator, or large-scale cloud systems.

• Deep hands-on expertise in at least one AI/ML networking domain, such as scale-up interconnects and silicon/SerDes/PHY, electrical or optical connectivity, switch systems, scale-out Ethernet/RDMA fabrics, transport/congestion control, collective communication, or workload/fabric performance.

• Demonstrated experience leading a complex hardware/software product or subsystem through requirements, architecture, development, integration, validation, and production deployment.

• Experience architecting or delivering both scale-up and scale-out networks for large AI training or inference systems.

• Experience with high-speed electrical and optical technologies, SerDes/PHY, FEC, signal integrity, link training, retimers, cable/backplane design, pluggable or co-packaged optics, and 400G/800G or faster links.

• Experience with switch ASICs and systems, NICs/DPUs, Ethernet, RDMA/RoCE, routing, QoS, ECN/PFC/DCQCN or related congestion-control methods, SONiC/Linux networking, and network operating systems.

• Understanding of collective communication libraries and operations, distributed training parallelism, AI workload traffic patterns, and workload-driven network optimization.

• Experience with network and system modeling, simulation, emulation, performance analysis, topology studies, traffic generation, or collective/workload scaling analysis.

• Experience developing automated validation, stress, reliability, interoperability, telemetry, diagnostics, observability, and fleet-health frameworks.

• Track record of system bring-up and cross-layer debug from physical links through protocol, runtime, collectives, and application behavior.

• Experience driving manufacturing readiness, supplier/partner execution, datacenter deployment, serviceability, and fleet operations for hardware-intensive products.

• Strong product judgement, technical leadership, collaboration, written communication, and executive presentation skills.

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.