Principal Fabric/Network Architect - Sunnyvale, California, United States

Originally posted 11 August 2026 by the employer.

Own the datacenter scale-out architecture for our T100 platform.

About the role

This role involves owning the datacenter scale-out architecture for the T100 platform, encompassing the entire data path from the Linux host networking stack through the electronic integrated circuit acting as a PCIe bridge, to third-party NICs and DMA into HBM. The T100 platform utilizes silicon photonics and an active, programmable metasurface for matrix multiplications.

What you'll do

• Own the end-to-end scale-out data path architecture spanning host network stack, EIC PCIe bridge, NIC attachment, and DMA into HBM.
• Define the transport and offload architecture, including RoCEv2 (RDMA over Converged Ethernet v2) and Ultra Ethernet Transport (UET).
• Specify peer-to-peer DMA topology and access control services (ACS) policy across the PCIe hierarchy.
• Drive NIC vendor selection and own the technical engagement with networking silicon partners.
• Model and evaluate collective communication performance for distributed inference workloads.
• Own the architectural case and roadmap for a future Neurophos DPU.

What you'll need

• 10+ years in datacenter networking architecture, including RDMA transports deployed at scale.
• Deep working knowledge of the Linux host networking stack: kernel networking, VFIO, DPDK or libfabric, and IOMMU/IOVA behavior.
• PCIe fluency: root complex versus endpoint roles, bridges, peer-to-peer transactions, ACS, and Gen5/Gen6 signaling and protocol.
• Familiarity with merchant NIC and switch silicon.
• Demonstrated ability to write architecture specifications that digital design and software teams can implement against.

Nice to have

• Participation in the Ultra Ethernet Consortium (UEC) or a comparable standards body.
• DPU or SmartNIC architecture experience.
• Congestion control design and evaluation.
• Experience bringing a novel accelerator into a production datacenter network.
• Familiarity with collective communication libraries (NCCL, RCCL, or equivalent) and distributed inference serving.
• Understanding of ML inference workload characteristics and their network demands.
• Experience with hyperscaler qualification and deployment processes.

Skills: datacenter scale-out architecture, RDMA transports, Linux host networking stack, PCIe protocol, NIC attachment