Full-Chip Verification Engineer
NVIDIA
Full-Chip Verification Engineer
NVIDIA
Designs the chips and software that power AI
We are looking for Full-Chip Verification Engineer to join our NVIDIA’s Networking Silicon team. In this role, you will be responsible for the development and verification of our next-generation NICs at the system level. You will contribute to the architecture of high-speed communication devices by driving full-chip verification execution for the networking solutions powering the world’s most advanced data centers, enjoy working in a meaningful, growing environment where you make a huge impact in a technology-focused company.
What you’ll be doing:
Full-Chip Verification & Execution: Own system-level features by defining verification plans and driving the end-to-end execution.
System-Level Debug & Analysis: Gain a strong understanding of chip micro-architecture and features, investigate, debug, and resolve cross-block issues to guarantee feature correctness and compliance
Cross-Team Collaboration: Work closely with multiple teams within organizations such as Architecture, u-arch, Firmware and all units inside the NIC
AI-Enhanced Development: Accelerate development by leveraging cutting-edge AI coding tools and frameworks.
What we need to see:
Electrical Engineering B.Sc. or Computer Engineering B.Sc. graduate with high scores or equivalent experience
3-5 years of experience in Verification or HW simulation
Strong debugging, problem solving and analytical skills
Innovation Mindset - a proactive approach to adopting new methodologies and coding tools to solve complex challenges
A team player with good communication and interpersonal skills
Ways to stand out from the crowd:
Prior verification experience of SoC
Knowledge in network flows and protocols
NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.
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