GPU System Performance Architect

NVIDIA; United States; On-site; USD 224,000 - 357,000; Full time

Job summary

NVIDIA is seeking a GPU System Performance Architect to design next-generation GPU-accelerated computing systems for data centers and cloud workloads. You will analyze trade-offs in system performance, cost and efficiency using analytical models, simulators and data visualization tools, and you will work with software, product and business teams across the company.

We value deep knowledge of computer architecture, strong math and communication skills, and a collaborative mindset to guide the

Qualifications

• Masters or PhD in CS/EE/CE (or equivalent).
• 10+ years of relevant work or research experience.
• Strong mathematics and analytical abilities.
• Deep knowledge of computer architecture.
• Excellent communication and teamwork skills.

Responsibilities

• Develop innovative processor and system architectures for GPU-accelerated cloud computing.
• Analyze performance, cost and efficiency with models, simulators and data visualization tools.
• Understand hardware-software interactions in large-scale GPU data centers.
• Collaborate across software, product and business teams to shape direction.

Skills

Computer architecture

Analytical skills

Strong communication

Team collaboration

Performance modeling

Simulation

Education

Masters or PhD in Computer Science, Electrical Engineering, or Computer Engineering

Job description

We are now looking for a GPU System Performance Architect:

The NVIDIA Architecture group is looking for extraordinary computer architects with a real passion for GPU-accelerated deep learning, data analytics and high performance computing to help design and develop the next generation of GPU-accelerated computing systems. This position offers the opportunity to have a real impact in a dynamic, technology-focused company.

What you'll be doing:

• Develop innovative processor and system architectures to extend the state of the art in GPU-accelerated cloud computing.
• You’ll analyze trade-offs in system performance, cost and efficiency by developing analytical models, simulators and data visualization tools.
• Understand and analyze the interactions between hardware and software in large-scale data center deployments of GPU-accelerated systems.
• Collaborate across the company to guide the direction of GPU-accelerated cloud computing, working with software, product and business teams.

What we need to see:

• You have a Masters or PhD in a relevant discipline such as Computer Science, Electrical Engineering, or Computer Engineering (or equivalent experience) with 10 years of relevant work or research experience.
• Excellent mathematical and analytical skills.
• Work experience that shows a deep knowledge of computer architecture.
• Strong communication, organizational and interpersonal skills with and a real passion for working as a team
• Experience with analytical performance modeling, simulation, profiling and analysis.

Ways you can stand out from the crowd:

• You possess a background in data center/cloud computing design with experience in crafting hardware for a virtualized environment.
• Expertise in data analysis and visualization.
• Prior experience and familiarity with GPU computing and parallel programming models.
• Prior experience in performance modeling, characterization and optimization of PCIe and IO-centric workloads.

GPU computing is the most productive and pervasive platform for deep learning and AI. It begins with the most advanced GPUs and the systems and software we build on top of them. We integrate and optimize every deep learning framework. We work with the major systems companies and every major cloud service provider to make GPUs available in data centers and in the cloud. And we create computers and software to bring AI to edge devices, such as self-driving cars and autonomous robots. With deep learning, we can teach AI to do almost anything. New internet services, like Google Assistant, have learned speech from sound and provide a more natural way to access information. Self-driving cars use deep learning to recognize the space the car inhabits, the lanes in which it drives, and the objects it must avoid. In healthcare, neural networks trained with millions of medical images can find clues in MRIs that until now could only be found through invasive biopsies. These are just a few examples. AI will spur a wave of social progress unmatched since the industrial revolution.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most brilliant and talented people in the world working for us. Are you creative and autonomous? Do you love a challenge? If so, we want to hear from you.

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.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.

Applications for this job will be accepted at least until September 18, 2026.

NVIDIA uses AI tools in its recruiting processes.