Job description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are looking for a Senior Solutions Architect to support our Semiconductor accounts — the chip-design houses, EDA software vendors, semiconductor-equipment makers, and fabs adopting accelerated computing across chip design, verification, lithography, and manufacturing. In this role you will be a trusted technical advisor to EDA/CAD developers and customer engineering teams, embedding NVIDIA accelerated computing, computational lithography (cuLitho), and AI into design, verification, and manufacturing workflows. You will play a direct role in improving application performance, accelerating design and yield cycles, and establishing the technical foundation required for next-generation semiconductor systems. What you’ll be doing: • 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. What we need to see: • 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. NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking individuals in the world working for us. If you're creative and autonomous, we want to hear from you! #NALASAHiring Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until July 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

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.
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.
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.
#NALASAHiring
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until July 20, 2026.

Nice-to-haves

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Keywords to mirror

aimlpythonreactkubernetes

Platform: eightfold
Effort: medium

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