Hire AI Chip Architects Who Hit the Utilization Target

The roadmap promised the throughput. The architecture leaves the systolic array idle and starves on HBM bandwidth, and the NPU never reaches the utilization the deck sold. On ShawSilicon you read the category score before you read the resume: every AI chip architect in the pool has passed a structured 10-question technical interview in their specialization, scored category by category — conceptual depth, design, debugging, diagrams, adversarial debug — against a fixed pass floor, and stays invisible to you until they clear it. You see where the tensor-core dataflow and bandwidth answers landed, then you decide.

Charter clients at locked pricing Verified shortlist in 72 hours for an eligible role, or no charge Scored by category vs a fixed pass floor Engineers keep 100% — zero commission
Get your first verified shortlist → Join as an engineer →

Send one eligible role brief and get a verified shortlist within 72 hours, or a straight answer that the verified depth is not there yet. Either way, no charge for that shortlist. Eligible = a role in one of the nine specializations with a clear brief (stack, level, must-haves).

9
SPECIALIZATIONS
10
QUESTIONS PER SCREEN
Scored
SHORTLIST BY CATEGORY
8–15%
PLATFORM FEE

Why AI Chip Architecture Hiring Is Broken

AI chip architects design the specialized hardware accelerators driving the AI revolution: neural processing units, tensor cores, systolic arrays, and the memory hierarchies that feed them. This is the fastest-growing specialization in semiconductor design.

AI chip architecture requires a unique blend of machine learning understanding and hardware design expertise. Engineers must optimize for inference latency, training throughput, power efficiency, and memory bandwidth simultaneously. ShawSilicon verifies this rare combination of skills.

Key Skills & Tools

Core Skills

NPU/TPU ArchitectureTensor Core DesignSystolic ArraysHBM/HBM2E IntegrationDataflow OptimizationQuantization-Aware DesignOn-Chip InterconnectsDMA Engines

EDA Tools & Platforms

SystemCTLMGem5NVDLATensorRTCustom RTL frameworks

Roles We Fill

How ShawSilicon Works

Step 1: Post your AI Chip Architecture role with required skills, rate range, and timeline.

Step 2: ShawSilicon matches you with verified engineers who have passed the structured technical interview in AI Chip Architecture. You see the category-by-category score, not just a resume.

Step 3: You interview the shortlist and start the engagement.

Who Sets the Bar

The same engineer designs the interview behind every specialization on ShawSilicon. It is built by John Bagshaw, a Senior FPGA Design Engineer with 8+ years designing for AMD, Intel, and Xilinx platforms — Zynq UltraScale+ and Agilex 7. The benchmarks behind the bar are public and timing-closed: cxl-kv-forge-qos at 400 MHz (WNS +0.413 ns, WHS +0.017 ns), a GNSS spoof/jam detector at 488.76 MHz, and flashattn-softmax and kvcache-compress both closed at 400 MHz. The fixed pass floor every engineer clears is the bar he holds himself to.

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Frequently Asked Questions

What AI chip skills does ShawSilicon test?
Our structured technical interview covers accelerator architecture concepts, dataflow optimization, memory hierarchy design for ML workloads, quantization-aware hardware, and the trade-offs between different compute array topologies (systolic, CGRA, dataflow).
Do your engineers have experience with specific AI accelerator platforms?
Yes. Our engineers have worked on custom NPUs, NVDLA-based designs, Google TPU-style architectures, and FPGA-based AI accelerators. Many have experience with both training and inference hardware.
How much does it cost to hire an AI chip architect on ShawSilicon?
ShawSilicon charges companies a platform fee of 8–15%, versus the 25–50% placement fee charged by traditional semiconductor recruiting agencies. Engineers keep 100% of their billings — zero commission. Each engineer sets their own rate.
Can I hire AI chip architects for FPGA-based accelerators?
Yes. Many AI chip architects on ShawSilicon have experience implementing neural network accelerators on FPGAs using HLS, custom RTL, or hybrid approaches.
What memory technologies do your AI chip engineers work with?
Our engineers have experience with HBM, HBM2E, LPDDR5, GDDR6, and on-chip SRAM hierarchies. Memory bandwidth optimization is a core competency for AI chip design.

Other Semiconductor Specializations