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. Are you ready to build the future of data processing with NVIDIA? As a Senior Architect in Data Processing Systems for Analytics and Data Intelligence (ADI), you'll define and improve hardware and software architectures for diverse set of data processing workloads. Join us in our mission to deliver outstanding performance at scale, using our accelerated data processing platform that combines CPUs, GPUs, high-bandwidth networking, and more! What you'll be doing: • Co-design future architectures for NVIDIA's data processing platforms, targeting performance, efficiency, and cost. • Engage deeply with end users, open-source communities, ISVs, storage partners, and CSPs to improve their experience by incorporating customer insights into roadmaps and reference architectures. • Drive full-stack performance optimization, from CUDA kernels and accelerated libraries to system-level IO, shuffle, compression, and data movement. • Collaborate with CPU, GPU, and platform architects to shape hardware features using workload analysis for hardware-software co-design. • Partner with internal teams to build and apply constraint-based frameworks and optimization solvers to navigate complex design trade-offs across competing metrics. • Develop benchmarking frameworks to evaluate performance sensitivities and guide hardware-software co-optimization • Analyze production workloads throughout the data processing landscape to identify compute motifs, IO patterns, and optimization opportunities across the full platform stack. What we need to see: • MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience. • 8+ years of experience in distributed data platforms, databases, systems engineering, performance architecture, accelerated computing, or HW/SW co-design. • Extensive knowledge in one or more of these areas: data processing systems (databases, query engines, or vector search), accelerated computing, high-performance IO, CPU/GPU architecture, or large-scale performance analysis. • Experience evaluating system-level trade-offs: compute, IO provisioning, memory subsystem development, network topology, and storage tiering across diverse workloads a strong plus. • Ability to translate sophisticated system analysis into clear, actionable recommendations for silicon architects, software engineers, and executive leadership. • Ability to work across matrixed organizations to drive alignment and deliver results. Ways to stand out from the crowd: • Hands-on experience with GPU-accelerated data processing (cuDF, RAPIDS, cuVS, CUDA). • Deep familiarity with structured data engines and/or vector database systems. • Experience with mathematical optimization solvers and understanding of optimization fundamentals. • Background in network/storage architecture. 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 October 20, 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

Co-design future architectures for NVIDIA's data processing platforms, targeting performance, efficiency, and cost.
Engage deeply with end users, open-source communities, ISVs, storage partners, and CSPs to improve their experience by incorporating customer insights into roadmaps and reference architectures.
Drive full-stack performance optimization, from CUDA kernels and accelerated libraries to system-level IO, shuffle, compression, and data movement.
Collaborate with CPU, GPU, and platform architects to shape hardware features using workload analysis for hardware-software co-design.
Partner with internal teams to build and apply constraint-based frameworks and optimization solvers to navigate complex design trade-offs across competing metrics.
Develop benchmarking frameworks to evaluate performance sensitivities and guide hardware-software co-optimization
Analyze production workloads throughout the data processing landscape to identify compute motifs, IO patterns, and optimization opportunities across the full platform stack.
MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
8+ years of experience in distributed data platforms, databases, systems engineering, performance architecture, accelerated computing, or HW/SW co-design.
Experience evaluating system-level trade-offs: compute, IO provisioning, memory subsystem development, network topology, and storage tiering across diverse workloads a strong plus.
Ability to translate sophisticated system analysis into clear, actionable recommendations for silicon architects, software engineers, and executive leadership.
Ability to work across matrixed organizations to drive alignment and deliver results.
Hands-on experience with GPU-accelerated data processing (cuDF, RAPIDS, cuVS, CUDA).
Deep familiarity with structured data engines and/or vector database systems.
Experience with mathematical optimization solvers and understanding of optimization fundamentals.
Background in network/storage architecture.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 20, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

Platform: eightfold
Effort: medium

Benefits

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