About the role
Normal Computing | Exceptional Career Opportunities
At Normal Computing, we pioneer state-of-the-art software and hardware solutions that drive technological advancements across the semiconductor industry, critical AI infrastructure, and the intricate systems that sustain our world. Our collaborative team spans across cities like New York, San Francisco, Copenhagen, Seoul, and London.
Your Role in Our Vision:
As a Hardware Engineer specializing in Silicon Design, you will be instrumental in defining and executing the architecture and microarchitecture of innovative AI compute blocks. This position merges the realms of machine learning algorithms, computer architecture, and RTL implementation. A profound understanding of contemporary AI workloads is essential for making informed hardware design decisions and translating these designs into production-grade RTL.
Key Responsibilities:
Design microarchitectures for cutting-edge AI accelerator blocks through close collaboration with architecture and research teams, converting algorithmic requirements into efficient hardware solutions.
Develop high-quality RTL in SystemVerilog for core logic, data paths, and control structures optimized for AI and ML workloads.
Maintain up-to-date knowledge of advanced AI algorithms and architectures, analyzing their computational patterns and hardware consequences.
Evaluate existing AI accelerator architectures and apply insights gained to tackle new design challenges.
Collaborate with the digital verification (DV) team on verification for assigned designs, including testbench creation, debugging, coverage, and signoff.
Partner with physical design engineers to ensure the RTL designs are feasible, high-performing, and compliant with layout constraints.
Contribute to functional or performance models that facilitate early exploration, validation, and design trade-off evaluations.
Engage in design and verification reviews, as well as cross-functional debugging from initial concept to silicon realization.
This position demands both a broad understanding of the AI accelerator landscape and a deep expertise in implementing complex digital logic. You should be adept at interpreting ML research papers and assessing their hardware implications while being hands-on with RTL coding, verification, and physical design constraints.
Why You Would Be a Great Fit:
BS, MS, or PhD in Electrical/Electronic Engineering, Computer Engineering, Computer Science, or a related discipline.
Experience with hardware design and RTL coding, particularly in the context of AI and machine learning.
