GPU Performance Profiling Engineer
NVIDIA
GPU Performance Profiling Engineer
NVIDIA
Designs the chips and software that power AI
At NVIDIA, we build groundbreaking products for the following sectors: Automotive, VR, Gaming, Deep Learning, and High Performance Computing. See your efforts in action as developers profile and analyze the performance of their applications using software and hardware that you helped craft as a member of the GPU Foundations Developer Tools team!
Innovate as you develop the performance analysis capability of NVIDIA hardware, drivers, and SDKs to support current and future generations of GPUs and SoCs. Help inventors around the world who will depend on the creative products that you worked on to better utilize our GPU hardware to its maximum potential in their applications. Join the team that brings new technologies to market. You will have a direct impact on the performance profiling tools that our team provides based on next generation hardware which you help to define.
What you'll be doing:
Analyze hardware/software feature sets for next generation GPU and SoC performance tools.
Review next generation chip features for high-quality developer tools support, ensuring observability of critical performance behaviors.
Collaborate with developer tools engineers, software library developers, and GPU architects on new features.
Prototype new data collection methods to improve GPU dev tools quality and reduce time/cost to market.
Refine data collection with NVIDIA's GPU Architecture and Developer Technology teams.
What we need to see:
B.S. EE/CS (or equivalent experience) with 4+ years of experience or MS with 2+ years' experience, or Ph.D.
Demonstrated expertise in the software stack, encompassing C/C++/Python applications and both user-mode and kernel-mode drivers.
Proven knowledge of CPU and/or GPU architecture (e.g.: x86, ARM CPUs, GPUs).
Solid understanding of hardware pipeline concepts, with a willingness to work at a detailed implementation level.
Knowledge of performance analysis concepts, particularly of GPU applications.
Proficiency in hardware-software co-design principles and practices.
Ways to stand out from the crowd:
Strong programming ability in C, C++, and scripting languages.
Strong background in performance analysis tools and performance monitor hardware.
Experience in CUDA or GPU programming models.
Prior experience authoring developer tools, particularly for GPUs, games, or pro visualization.
In-depth experience with profiler tools including CPU, GPU, and networking as well as experience in device driver development.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most dedicated and hard-working individuals in the world working for us. If you are creative and autonomous, we want to hear from you. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people in multiple disciplines to help us accelerate the next wave of computing.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:You will also be eligible for equity and benefits.
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.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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