Senior GPU Architect - Performance and Yield Optimization
NVIDIA· Architect
Santa Clara, CA$184k - $356.5kFull-timePosted 1mo ago
About the role
Senior-level role focused on shaping GPU architectures to improve product yield while maintaining performance, developing software tools and models, and collaborating across teams to move proposals into production.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Required:GPU architectureC++PythonOptimization / constraint-based reasoningPerformance and cost tradeoffs
Nice to have:Yield analysis / defect toleranceMulti-die / chiplet architectures
Benefits
- Equity
- Benefits (as listed in NVIDIA benefits page)
Last updated Oct 16, 2026. Interested? Apply on the company site.
About NVIDIA
World Leader in AI Computing
NVIDIA invents the GPU and drives advances in AI, HPC, gaming, creative design, autonomous vehicles, and robotics.
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