System Software Engineer - Performance Verification Infrastructure
at Nvidia
USD 124,000-241,500 per year
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 2
Algorithms @ 6
Data Pipelines
Data Structures @ 6
GPU @ 3
Profiling @ 6
Python @ 5
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Responsibilities
- Build and develop end-to-end performance verification infrastructure.
- Build scalable data pipelines to surface performance metrics from simulation, emulation, and silicon environments.
- Coordinate with GPU architects to craft infrastructure for performance verification of upcoming architectures.
- Work closely with HW teams to automate and accelerate verification workflows, enabling faster GPU build iteration.
- Empower GPU architects by providing insights to understand current performance and model industry-leading performance for future builds.
- Improve the daily workflows of the world's top chip architects, contributing to the creation of the next greatest generation of GPUs!
Requirements
- BS/MS in Computer Science, Computer Engineering, or equivalent experience.
- 3+ years of software engineering experience with C++ and Python.
- Strong CS fundamentals, including data structures, algorithms, OOP, and system design.
- Verilog basics is a strong plus.
- Experience with chip and/or system simulation/emulation a strong plus.
- Exposure to GPU/CPU/SoC chip performance verification and/or profiling on simulation/emulation/silicon is a strong plus.
- Familiarity with AI development tools.
- Excellent interpersonal skills.
Benefits
- You will also be eligible for equity and benefits.
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