Senior System Architect

NeuReality is seeking a Lead System Architect to join our system architecture team and help define the next generation of our AI-SuperNIC scale-out chip.AI scale-out communication is a critical element in modern data centers, and emerging standards such as Ultra Ethernet aim to address this challenge. This role focuses on defining a high-performance Smart NIC architecture optimized for GPU-centric AI workloads, with emphasis on low-latency, high-bandwidth data movement.You will work across hardware and software domains, collaborating closely with AI, platform, driver, and VLSI teams to design a competitive scale-out networking solution.ResponsibilitiesDefine product requirements and architecture for NeuReality’s next-generation ultra low-latency AI-SuperNICDesign end-to-end data paths across NIC, host, and GPU memory, with focus on GPU connectivity (PCIe, NVLink, CXL)Lead adoption of emerging standards such as UEC and UALink, while maintaining compatibility with existing ecosystemsEstablish architecture guidelines and partitioning across hardware, firmware, and softwareDrive performance modeling, analysis, and system-level optimization for AI workloadsOptimize scale-out communication patterns (e.g., collectives) for GenAI training and inferenceCollaborate with software teams on drivers, runtimes, and integration with AI frameworksWork with R&D, CTO, and peer architects on system definition and trade-offsEngage with customers and partners to shape system requirements and use casesRequirements:BSc/MSc in Electrical Engineering, Computer Science, or related field5+ years of experience as a system architect on complex SoCs or data center systemsStrong experience with large-scale systems integrating hardware, firmware, and softwareBackground in high-performance systems, networking, or data center infrastructureExperience working with GPU-based systems or AI/ML workloadsKnowledge of high-speed interconnects such as PCIeNice to haveExperience with UEC, UALink/NVLink, RoCEv2, NVMe-oF, or RDMA technologiesFamiliarity with IP networking, TCP/UDP, or network securityExperience with in-network compute (e.g., SHARP, INC)Deep understanding of GenAI/ML infrastructure and distributed workloads