We're building a new generation of data center CPUs at NVIDIA — Vera, Grace, and future designs purpose-built for accelerated computing and the workloads defining infrastructure. Data analytics is one of the highest-value arenas for CPU performance, and we need someone to own NVIDIA's strategy and value proposition there! This role sits at the nexus of go-to-market, software architecture, platform architecture, performance measurement, and competitive positioning — making it a true workload strategy role, not a benchmark-running one. From TPC-H and TPC-DS methodologies to collaborative solutions with the world's leading database and analytics vendors, the scope is broad and the impact is real. We're looking for a product manager who deeply understands how CPUs win in practice and can turn that insight into roadmap decisions, customer proof points, and field-ready positioning.
What you'll be doing:
- Taking responsibility for the workload and market approach for data analytics and data processing on NVIDIA data center CPUs — identifying priority workloads, customer scenarios, and the benchmarks and proof points that should guide the roadmap.
- Leading the complete lifecycle of benchmarks like TPC-H and TPC-DS: methodology, cluster and benchmarking architecture, competitive analysis, and market positioning.
- Translating disk, cache, memory, I/O, clustering, availability, and software behavior into concrete CPU, platform, and system requirements. Then advising architecture and platform teams on what to build and why.
- Building the quantitative value proposition across performance, infrastructure, software licensing, power, and TCO to position NVIDIA CPUs against challenging architectures.
- Working alongside external ISVs, database and analytics vendors, cloud providers, and strategic customers to verify requirements and build joint solutions and go-to-market approaches.
- Redefining benchmark results and customer evidence into product decisions, competitive reactions, customer proof points, and field enablement materials.
What we need to see:
- 12+ years of experience in technical product management, performance engineering, or a closely related area.
- BS or MS in Computer Science, Electrical or Computer Engineering — or equivalent experience.
- Comprehensive, practical knowledge of CPU and database performance.
- Understanding of system hardware characteristics that influence analytics performance — caching and clustering methods, high availability, memory and I/O sensitivity, and architectural trade-offs.
- Ability to influence platform and architecture teams by translating workload performance into product, platform, and feature requirements.
- Experience collaborating with external ISVs, silicon vendors, or the broader analytics ecosystem.
Ways to stand out from the crowd:
- Hands-on experience with TPC-H, TPC-DS, or comparable data analytics benchmarks, including methodology development and measurement.
- Understanding of the disk, cache, and memory sensitivities of database and analytics workloads at scale.
- Experience with market entry strategies, solution presentation, or the wider data analytics provider and cloud environment.
- Background in accelerated computing platforms or data center CPU product launches.
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.Skills Required
- 12+ years of experience in technical product management, performance engineering, or a closely related area
- BS or MS in Computer Science, Electrical Engineering, Computer Engineering, or equivalent experience
- Comprehensive practical knowledge of CPU and database performance
- Understanding of caching, clustering, high availability, memory, I/O sensitivity, and architectural trade-offs affecting analytics performance
- Ability to translate workload performance into product, platform, and feature requirements
- Experience collaborating with external ISVs, silicon vendors, or the broader analytics ecosystem
- Hands-on experience with TPC-H, TPC-DS, or comparable data analytics benchmarks
- Understanding of disk, cache, and memory sensitivities of database and analytics workloads at scale
- Experience with market entry strategies, solution presentation, or the data analytics provider and cloud environment
- Background in accelerated computing platforms or data center CPU product launches
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
