What you’ll be doing:

Lead all aspects of implementing performance practices in large scale infrastructure, deliver powerful tools, methodologies, and flows to validate and improve several datacenter products in parallel.

Accelerate strategic customer deployments and ensure speed-of-light bringup and deployment of ground-breaking AI infrastructure by working hand in hand tailoring design and faster processes to customer needs.

Specific responsibilities include owning the architecting of performance design and settings of datacenter at scale products both implemented in FW and SW components to ensure velocity and scale while efficiently using resources. This involves early engagement with HW/FW/SW/platform internal and customer teams, and other groups, to build end-to-end solutions and optimize datacenter product designs.

As a key member you will supply to architecting of the implementation of server and rack level telemetry aspects, collaborate and establish continuous improvements in our design flows.

Participating in engagements with various SW and FW (BMC/SBIOS/OS/drivers etc) teams to develop best-in-class practices and tools, you will be analyzing, debugging and resolving critical firmware and software issues for the best AI workload performance at scale.

Provide engineering solutions to enable large scale performance strategies for performance for Datacenter GPU Computing products and software stacks, ensure technical relationships with internal and external engineering teams, and assisting systems engineers in building creative solutions based on NVIDIA technology.

Be an internal reference for firmware, at scale deployment for datacenter and large-scale GPU-accelerated system solutions among the NVIDIA technical community.

What we need to see:

5+ years of experience in using accelerated computing for datacenter container computing solutions.

Strong knowledge of accelerated computing software stacks (CUDA).

Experience using and handling modern Cloud and container-based Enterprise computing architectures.

C/C++/Python/Bash programming/scripting experience.

Experience with CPU architecture.

Experience with container technology and Linux based OSes.

Experience working with engineering or academic research community supporting high performance computing or deep learning.

Strong verbal and written communication skills.

Strong teamwork and social skills.

Ability to multitask effectively in a dynamic environment.

Action driven with strong analytical and analytical skills.

Desire to be involved in multiple diverse and creative projects.

BS in Engineering, Mathematics, Physics, or Computer Science (or equivalent experience). MS or PhD desirable.

Ways to stand out from the crowd:

Deep Learning framework skills.

DL and graph compiling programming skills.

Exposure to virtualization techniques, cloud platform solutions.

Exposure to scheduling and resource management systems.

Experience with high performance or large scale computing environments.