> Markdown version of [/jobs/ext/2283696-senior-ai-solutions-architect-semiconductors](https://www.wearedevelopers.com/jobs/ext/2283696-senior-ai-solutions-architect-semiconductors). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Solutions Architect - Semiconductors - **Company:** NVIDIA Corporation - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $152,000.0 - $241,500.0 - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Artificial Intelligence, Computing Platforms, C++ (Programming Language), Nvidia CUDA, Computer Engineering, Distributed Computing Environment, Distributed Systems, Field-Programmable Gate Array (FPGA), Github, Python (Programming Language), PCI Express, Tensorflow, Software Engineering, Linux Virtual Server, Graphics Processing Unit (GPU), Pytorch, ReactJS, Yield Optimization, Kubernetes, Power Analysis (Cryptography), Physical Design, Software Version Control, Data Pipelines, Software Library - **Published:** August 28, 2026 - **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-AI-Solutions-Architect---Semiconductors_JR2020230 ## About the Role * MS/PhD in Electrical or Computer Engineering, Materials Science, Applied Physics, Computational Science, or a related technical field (or equivalent experience). * 4+ years in semiconductor design, EDA, or semiconductor manufacturing - chip design/verification, TCAD, lithography, or fab process/yield engineering - and/or AI/ML applied to these domains. * Familiarity with EDA flows and tools (e.g., Cadence, Synopsys, Siemens EDA) and/or computational lithography, TCAD, or inspection/metrology systems. * Experience in algorithm programming using languages like Python and C/C++, with familiarity GPU-accelerating compute-intensive workloads. * Development experience using major AI frameworks (e.g., PyTorch, TensorFlow) for vision, ML, or manufacturing use cases. * Familiarity with accelerated computing platforms, GPU-based distributed systems, and HPC clusters. * Familiarity with containers, numerical libraries, modular software design, version control, GitHub. * Experience designing, prototyping, and building complex AI/ML-based solutions for customers; able to reason across components such as data pipelines, models, compute, networking, and orchestration. * Solid written and oral communication skills and familiarity with collaborative environments. * Team player who can learn, react, and adapt quickly, with an attitude to work in a fast-paced environment. Ways to stand out from the crowd: * Experience with computational lithography (NVIDIA cuLitho) or GPU-accelerated EDA flows. * Experience applying ML/DL to defect inspection, metrology, or yield and process optimization in a fab or equipment setting. * Development experience with NVIDIA software libraries and GPUs, including CUDA and CUDA-X libraries. * Experience with Kubernetes, distributed training, and large-scale inference. * Experience supporting or using PCIe accelerators such as GPUs, FPGAs, DSPs from evaluation to production stages. ## Description * Support Business Development and Sales teams as part of a small Solutions Architecture team, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive ecosystem success across Semiconductor accounts (EDA vendors, chip designers, semiconductor-equipment OEMs, and fabs). * Work directly with EDA/CAD developers and customer design and manufacturing teams in a customer-facing setting. * Help developers GPU-accelerate and scale EDA workflows - place-and-route, circuit simulation, timing and power analysis, DRC/LVS, and verification - and computational lithography (e.g., NVIDIA cuLitho). * Apply ML/DL to semiconductor manufacturing: defect detection, inspection and metrology, yield optimization, and process control. * Analyze EDA and manufacturing application architectures and find opportunities for acceleration. * Provide feedback and collaborate with engineering, product, and research teams. * Deliver trainings, hackathons, and technical demonstrations on NVIDIA solutions and platforms. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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