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Hire AI Chip Architects
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
Evidence shortlist for an eligible role, free to review
Scored by category vs a fixed pass floor
Engineers keep 100% — zero commission
Send one eligible role brief and get either an evidence shortlist or a straight answer that the verified depth is not there yet. Reviewing it costs nothing. Eligible = a role in one of the nine specializations with a clear brief (stack, level, must-haves).
Scored
SHORTLIST BY CATEGORY
15–7%
PLACEMENT FEE BY TIER
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
- AI Chip Architect
- NPU Design Engineer
- ML Hardware Engineer
- AI Accelerator RTL Designer
- DNN Hardware Optimization Engineer
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 post-route (WNS +0.033 ns, TNS 0.000, hold +0.010 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.
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?
Engineers pay nothing and keep 100% of their rate. Companies pay a one-time direct-hire placement fee on first-year base salary, by tier: 15% Starter, 10% Growth, 8% Scale, 7% Enterprise, with Founding Cohort clients locked at 10%. Contract engagements carry a 15% hourly markup. Traditional semiconductor recruiting agencies charge 25% to 50%. 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