Platform Software Product Manager
| Company | NVIDIA |
|---|---|
| Location | Washington, USA, Santa Clara, CA, USA, Durham, NC, USA, Virginia, USA, Colorado, USA, Massachusetts, USA |
| Salary | $168000 – $327750 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior, Expert or higher |
Requirements
- 8+ years of total experience in technology with design management, engineering, or design experience highly valued
- BS or MS in engineering, computer science, or another technical field, or equivalent experience. MBA a plus.
- Demonstrated ability to fully contribute in one or more of the areas above within 3 months
- Direct technical knowledge of processor, software and server architectures
- Understanding SoC hardware design and typical interfaces
- Strong desire to learn, strong problem solving, and a developed ability to make sophisticated trade-offs
Responsibilities
- Develop a deep understanding of requirements for datacenter products, closely working with customers, industry specialists, and various teams in NVIDIA
- Own the complete Product lifecycle starting with product definition through development, release and maintenance
- Develop written product requirements which would include platform and device telemetry, software lifecycle management, and serviceability metrics
- Work with internal teams as well as our partners to ensure availability of key IP, tools, and software for product success
- Engage directly with our largest customers to gather feedback and requests, develop effective sales collateral and tools to scale the impact our products make
- Lead technical direction and roadmap for GPU and CPU product line, impacting NVIDIA’s growth.
Preferred Qualifications
- In-depth knowledge of server platform design, microprocessor design, and SoC firmware.
- Direct experience running GPU datacenter server programs a plus
- Recent experience in key data center technologies such as, server architectures, software containers, job schedulers and parallel computing. Deployment and operation of systems at large scale; resilient system design; and clustering compute resources along with networking infrastructure.