Job description

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. What You'll Be Doing: • Work with us to shape GPU architectures that improve product yield while maintaining performance and architectural simplicity. • Analyze how manufacturing defects affect architectural resources, then develop methods that isolate or disable affected regions while preserving useful functionality. • Build software tools and models that capture architectural, performance, and product requirements as rules and constraints. • Develop efficient optimization techniques that explore large configuration spaces and identify viable product configurations. • Develop approaches for multi-die architectures that improve utilization of available silicon while meeting product and performance requirements. • Evaluate yield, performance, area, implementation cost, complexity, and product flexibility, then work across architecture, performance, design, silicon, Operations, and software to move selected proposals into production. What We Need to See: • BS, MS, or PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field, or equivalent experience, plus 10+ years in GPU, CPU, SoC, or complex processor architecture. • Strong understanding of GPU architecture, the overall execution pipeline, and interactions across major GPU subsystems. • Strong computer architecture fundamentals and the ability to reason about disabling, isolating, or reconfiguring resources. • Strong C++ and/or Python skills with experience building architectural models, simulators, optimization frameworks, or engineering analysis tools. • Experience translating architecture and product requirements into rules, constraints, algorithms, and executable analysis, including complex optimization or configuration problems. • Ability to quantify performance, product yield, area, cost, and complexity tradeoffs and influence decisions across architecture, design, implementation, and product teams. Ways to Stand Out From the Crowd: • Hands-on GPU architecture or large-scale SoC architecture experience. • Experience with constraint-based reasoning, combinatorial optimization, mathematical optimization, or related techniques. • Background with silicon yield, defect tolerance, harvesting, redundancy, repair, resource isolation, or configurable processor architectures. • Experience with multi-die, chiplet, or other modular processor architectures and complex resource-allocation problems. • Experience using silicon or manufacturing data, developing performance or architecture simulators, profiling GPU workloads, or carrying concepts into production silicon. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 19, 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.

Requirements

Must-haves

Work with us to shape GPU architectures that improve product yield while maintaining performance and architectural simplicity.
Analyze how manufacturing defects affect architectural resources, then develop methods that isolate or disable affected regions while preserving useful functionality.
Build software tools and models that capture architectural, performance, and product requirements as rules and constraints.
Develop efficient optimization techniques that explore large configuration spaces and identify viable product configurations.
Develop approaches for multi-die architectures that improve utilization of available silicon while meeting product and performance requirements.
BS, MS, or PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field, or equivalent experience, plus 10+ years in GPU, CPU, SoC, or complex processor architecture.
Strong understanding of GPU architecture, the overall execution pipeline, and interactions across major GPU subsystems.
Strong computer architecture fundamentals and the ability to reason about disabling, isolating, or reconfiguring resources.
Strong C++ and/or Python skills with experience building architectural models, simulators, optimization frameworks, or engineering analysis tools.
Experience translating architecture and product requirements into rules, constraints, algorithms, and executable analysis, including complex optimization or configuration problems.
Ability to quantify performance, product yield, area, cost, and complexity tradeoffs and influence decisions across architecture, design, implementation, and product teams.
Hands-on GPU architecture or large-scale SoC architecture experience.
Experience with constraint-based reasoning, combinatorial optimization, mathematical optimization, or related techniques.
Background with silicon yield, defect tolerance, harvesting, redundancy, repair, resource isolation, or configurable processor architectures.
Experience with multi-die, chiplet, or other modular processor architectures and complex resource-allocation problems.
Experience using silicon or manufacturing data, developing performance or architecture simulators, profiling GPU workloads, or carrying concepts into production silicon.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 19, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Keywords to mirror

aipython

Platform: eightfold
Effort: medium

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