Job Description

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.

Responsibilities

• Define product requirements and architecture for NeuReality’s next-generation ultra low-latency AI-SuperNIC
• Design 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 ecosystems
• Establish architecture guidelines and partitioning across hardware, firmware, and software
• Drive performance modeling, analysis, and system-level optimization for AI workloads
• Optimize scale-out communication patterns (e.g., collectives) for GenAI training and inference
• Collaborate with software teams on drivers, runtimes, and integration with AI frameworks
• Work with R&D, CTO, and peer architects on system definition and trade-offs
• Engage with customers and partners to shape system requirements and use cases

Requirements

• BSc/MSc in Electrical Engineering, Computer Science, or related field
• 5+ years of experience as a system architect on complex SoCs or data center systems
• Strong experience with large-scale systems integrating hardware, firmware, and software
• Background in high-performance systems, networking, or data center infrastructure
• Experience working with GPU-based systems or AI/ML workloads
• Knowledge of high-speed interconnects such as PCIe

Nice to have

• Experience with UEC, UALink/NVLink, RoCEv2, NVMe-oF, or RDMA technologies
• Familiarity with IP networking, TCP/UDP, or network security
• Experience with in-network compute (e.g., SHARP, INC)
• Deep understanding of GenAI/ML infrastructure and distributed workloads