Senior System Software Engineer - Performance

We're searching for a highly motivated, technical engineer to join the Tegra system-on-chip (SoC) software organization. You will work on key aspects of our ARM SW ecosystem and system software architecture. With a targeted charter to enable best-in-class datacenter-scale performance and efficiency for our next generation of datacenter products, including CPUs and CPU+GPU Superchips.

Key Responsibilities:

• Design, develop, test, and optimize software for our next-generation SoCs. In both pre-silicon and post-silicon phases of execution.
• Review architectural performance bottlenecks for various system wide work loads. Identify HW/SW policies to drive performance and performance/watt leadership.
• Using strong communication skills, build and drive architecture, analysis documents and communications to internal and/or external audiences about our technology.
• Competitive analysis comparing uArchitecture & workload performance metrics on NVIDIA's ARM SoCs against emerging processors from other silicon vendors.
• Influence and drive full-stack adoption of performance optimizations and standard methodologies across NVIDIA SW products & OSS SDKs

Requirements:

• BS or MS degree in Computer Engineering, Computer Science, or related degree (or equivalent experience).
• 6+ years of relevant computer architecture or SW development experience.
• Proven leadership skills and strong ownership on past projects.
• Hands on technical experience and demonstrated excellence in an environment with complex software and hardware designs.
• Strong understanding of multicore hardware, operating systems design, concurrency, virtual memory, caching, interrupts, device drivers and real-time programming.
• Strong stills in performance analysis, data analysis and performance optimization.

Nice to Have:

• Deep expertise in ARM architecture and SW ecosystem.
• Proficient in analyzing, debugging and tuning performance of complex system software stacks.
• Experience with CPU server system workloads and performance analysis.
• Familiarity with CUDA programming and/or GPUs.
• Experience with HPC or large-scale computing environments.