We're redefining analytics for the agentic datacenter, where AI agents plan workflows, discover data, generate queries from natural language, choose execution paths, learn from experience, and orchestrate compute within the datacenter. This new era requires analytics systems that understand user intent and semantic context, handle sophisticated workflows, and route work intelligently across different hardware and systems based on latency, cost, scale, and other metrics depending on business need.
Want to help make this future a reality? We’re looking for an outstanding Senior Product Manager to define and drive NVIDIA’s strategy for agentic data analytics. You’ll identify the right opportunities for NVIDIA to invest in across models, skills, tools, CUDA-X libraries, services, partner integrations, and enterprise go-to-market motions, while keeping an open mind on prioritization as the market evolves rapidly.
What you'll be doing:
Define, own, and drive the product strategy for analytics in the agentic datacenter, connecting agent workflows, text-to-SQL, semantic context, query execution, and intelligent workload routing
Engage deeply with customers, partners, and open-source communities to discover underlying needs, owning the voice of the customer and incorporating insights into roadmaps and priorities
Establish success metrics and requirements for agentic analytics workflows where latency, throughput, cost, correctness, and infrastructure utilization all matter
Collaborate with Engineering, Research, DevRel, Field, and Enterprise teams to create agentic analytics architectures and reference blueprints
Serve as the point person for presenting vision, opportunities, and progress, setting expectations and managing internal and external collaborators
What we need to see:
BS or MS Degree in a quantitative field (e.g., Computer Science, Applied Math, Computational Science, Engineering, etc.) or equivalent experience
12+ years of product management, technical product strategy, or similar background in data analytics, data/AI infrastructure, or accelerated computing
Strong technical proficiency with databases, query engines, SQL, Python, and enterprise data platforms, with the ability to rapidly prototype agentic workflows, build proofs of concept, and get hands-on as a builder
Experience building agentic products or workflows for data analytics involving text-to-SQL, semantic layers, enterprise governance and security, iterative improvement cycles, etc.
Track record of crafting strategy in ambiguous technical markets, partnering across engineering/research/open-source ecosystems, and communicating value propositions clearly to technical and non-technical audiences
Ways to stand out from the crowd:
Experience building evaluation datasets and improving agentic analytics systems across critical factors, including generated query correctness, cost, latency, and failure recovery
Familiarity with query optimization and cost estimation, workload orchestration, or production scheduling in distributed data systems
Experience translating performance benchmarks, TCO models, or CPU/GPU tradeoffs into product strategy and customer-facing value propositions
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
- BS or MS degree in a quantitative field or equivalent experience
- 12+ years of product management, technical product strategy, or similar experience in data analytics, data/AI infrastructure, or accelerated computing
- Strong technical proficiency with databases, query engines, SQL, Python, and enterprise data platforms
- Ability to rapidly prototype agentic workflows, build proofs of concept, and be hands-on as a builder
- Experience building agentic products or workflows for data analytics (text-to-SQL, semantic layers, enterprise governance and security, iterative improvement)
- Track record of crafting strategy in ambiguous technical markets and partnering across engineering/research/open-source ecosystems
- Experience building evaluation datasets and improving agentic analytics across correctness, cost, latency, and failure recovery
- Familiarity with query optimization, cost estimation, workload orchestration, or production scheduling in distributed data systems
- Experience translating performance benchmarks, TCO models, or CPU/GPU tradeoffs into product strategy and customer-facing value propositions
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.”
.png)
