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系统软件工程师 - 性能验证基础设施

雇主

NVIDIA

地点

Santa Clara · 美国

待遇

$面议

工作模式

混合

截止日期

1月10日

福利
Equity,Benefits

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岗位摘要

Join NVIDIA's GPU Performance Infrastructure team to build end-to-end performance verification infra

岗位职责

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.
Join NVIDIA's GPU Performance Infrastructure team and be part of a brand-new journey in validating and shipping next-generation GPU architectures.
At NVIDIA, we empower our engineers to innovate and drive powerful performance through innovative infrastructure and workflows.
This role is uniquely positioned to influence the entire lifecycle of GPU performance verification, from early-stage modelling to post-silicon validation.
With our bold standards and extraordinary team, you'll have the chance to define a significant impact on the future of computing!
What you'll be doing:
• 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!
What we need to see:
•
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.
#LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.
You will also be eligible for equity and benefits. Applications for this job will be accepted at least until September 7, 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.

申请条件

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.

雇主简介

NVIDIA is a technology company that designs GPUs and system-on-chip units for gaming, professional visualization, data centers, and automotive markets. The Physics Simulation Team focuses on physics simulation for robotics learning and evaluation.

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