Drimva

Architect Labs

Microarchitect / RTL Design - Interconnect & Fabric

Palo Alto · On-site · Full-time · Posted Aug 12, 2026

Experience
5+ years; 10+ preferred. Requires at least one advanced-node tapeout involving on-chip interconnects, NoC fabrics, or high-speed I/O subsystems.
Education
Bachelor’s, Master’s, or PhD in Electrical Engineering, Computer Engineering, or a closely related field.

About the job

Own the AI-driven microarchitecture and RTL design of on-chip interconnect fabrics and high-speed I/O data-movement subsystems for production silicon.

What you'll do

  • Design NoC routers, interconnect fabrics, protocol bridges, and peer-to-peer data-transfer engines; take RTL through lint, CDC, synthesis, and timing closure.
  • Define fabric interfaces and specifications, and collaborate with architecture, verification, software, and ML teams on design trade-offs, validation, and improvements to the AI-driven design flow.
  • Build RTL automation and regression infrastructure, and support FPGA prototyping and performance characterization.

What they ask for

  • Bachelor’s, Master’s, or PhD in Electrical Engineering, Computer Engineering, or a closely related field.
  • 5+ years in RTL design and at least one advanced-node tapeout involving on-chip interconnects, NoC fabrics, or high-speed I/O subsystems.
  • Hands-on AMBA protocol and NoC/fabric design experience, including interconnects, arbitration, flow control, and QoS.
  • Experience with high-speed I/O integration, peer-to-peer DMA, synthesizable SystemVerilog, SoC implementation methodology, Python, and PPA closure.

Nice to have

  • 10+ years of RTL design experience.
  • Experience with coherent multi-chip interconnects, hardware coherence, NoC research, FPGA prototyping, SVA, performance modeling, or related IP delivery.

Tools

SystemVerilog, Python, AMBA AXI4/AXI5, ACE/ACE-Lite, CHI, PCIe, CXL, UCIe

AI focus

Own AI-driven microarchitecture and RTL design, use an AI-driven design flow, and work with software and ML teams to improve the in-house AI flow for traffic modeling and fabric configuration optimization.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 5, 2026 (UTC)