5 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Deploy AI toolsets at scale in secure configurations for hardware design teams.
- Use ML, deep learning, and AI techniques to automate infrastructure work and improve design-team productivity.
- Improve the speed, flexibility, and extensibility of the GPU front-end build flow.
- Maintain and advance continuous integration and pre-submit verification flows.
- Guide compute-farm, filer, and network-topology requirements at cloud scale.
- Deploy and improve container, volume-cloning, distributed-storage, and distributed-computing technologies.
- Forecast hardware-design compute resources and EDA needs, with reporting to the CFO’s office.
- Deploy operational tracking metrics, information-security methods, and infrastructure improvements for hardware design.
- Root-cause and resolve intermittent infrastructure problems and remove operational inefficiencies.
Requirements
- Master’s degree in electrical engineering, computer engineering, computer science, or a related field, or equivalent experience.
- At least 8 years of relevant work experience.
- Programming proficiency in Python, Perl, or another systems programming language; object-oriented design is preferred.
- Experience using AI tools for development and automation.
- Experience with Make-based build systems in large, distributed computing environments.
- Experience with continuous integration pipelines or pre-submit verification flows, such as Jenkins.
- Verification-domain knowledge for complex ASICs or CPUs, including random stimulus, functional coverage, and assertion-based verification methodologies.
- Strong problem-solving, debugging, analytical, interpersonal, and teamwork skills.
Benefits
- Eligible for equity and benefits.
- Base salary varies by location, experience, and other objective job-related factors.
- Applications are accepted at least until November 8, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.