This role pays less than
57%
of similar roles. Most pay
$171,450–$242,500
— the shaded band above.
At the midpoint, this role pays about
$197k
versus about
$207k
for comparable roles.
Based on 239 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior AI Solutions Architect - Semiconductors, you will join a specialized Solutions Architecture team to serve as a trusted technical advisor for semiconductor accounts, including chip-design houses, EDA software vendors, and equipment makers. You will work directly with customer engineering teams to integrate accelerated computing, computational lithography, and AI into design, verification, and manufacturing workflows. Your daily responsibilities include accelerating EDA flows like place-and-route and circuit simulation, applying machine learning to defect detection and yield optimization, and delivering technical demonstrations. You will utilize Python, C/C++, and major frameworks like PyTorch or TensorFlow to build complex solutions. The role requires expertise in GPU-accelerated workloads, CUDA libraries, and distributed systems to solve critical challenges in semiconductor manufacturing, lithography, and electronic design automation within a high-performance computing environment.
Act as a technical advisor to EDA/CAD developers and customer engineering teams to integrate NVIDIA technologies into workflows.
Help customers GPU-accelerate and scale EDA workflows including place-and-route, circuit simulation, and verification.
Implement computational lithography solutions like NVIDIA cuLitho for semiconductor manufacturing processes.
Apply machine learning and deep learning to manufacturing tasks such as defect detection, metrology, and yield optimization.
Analyze application architectures to identify specific opportunities for hardware acceleration.
Provide technical feedback to internal engineering, product, and research teams to improve NVIDIA offerings.
Deliver technical demonstrations, trainings, and hackathons regarding NVIDIA solutions and platforms.
Partner with sales and business development teams to drive success across semiconductor accounts and ecosystems.
What we're looking for
MS/PhD in Electrical or Computer Engineering, Materials Science, Applied Physics, Computational Science, or a related technical field (or equivalent experience).
4+ years of experience in semiconductor design, EDA, manufacturing, or AI/ML applied to these domains.
Familiarity with EDA tools and flows, computational lithography, TCAD, or inspection/metrology systems.
Proficiency in Python and C/C++ for programming algorithms and GPU-accelerating compute-intensive workloads.
Experience using major AI frameworks like PyTorch or TensorFlow for vision, ML, or manufacturing use cases.
Familiarity with accelerated computing platforms, GPU-based distributed systems, HPC clusters, containers, and version control.
Experience designing and prototyping complex AI/ML-based solutions involving data pipelines, models, compute, networking, and orchestration.
Strong written and oral communication skills for technical demonstrations and collaborating in fast-paced environments.