We are looking for a: Senior System Software Engineer – DC Platform SW Tools. 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 deep learning — 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, we are increasingly known as “the AI computing company.” We’re looking to grow our company and establish teams with the most thoughtful people in the world. NVIDIA Grace and GPU superchips provide performance and productivity required for strong scaling for HPC and generative AI workload. Scale out is inherent to the design of this massive superchip.
We are looking for a Senior System Software Engineer to join our Data Center Platform Software Tools team. You will be responsible for the design, development, enhancement, and deployment of tools in large-scale AI data centers. The primary focus of these tools is to provide simple user experience in the data center manageability life cycle from deployment, production, service, and repair workflows. You will work closely with cross-functional teams, including hardware engineers, system architects, software developers, and customers to gather requirements, create solutions and provide end-to-end simplified manageability experience. Are you ready to change the next generation of computing? Join us at the forefront of technological advancement.

What you’ll be doing:

• Drive next generation GPU Server Software manageability workflows for scaling AI infrastructure for Datacenters. This infrastructure includes DGX, HGX or MGX Products. You will be involved in ensuring proper tools are built for managing Server Software and Firmware for data center lifecycle.
• Work with internal and external customers to understand requirements for various tools to improve debuggability, serviceability and runtime of data center firmware and software.
• Contribute to all phases of product development, from product definition, architecture, and design, through implementation, debugging, testing and early customer support.
• Maintain detailed documentation of tool designs, capabilities, and usage guidelines. Provide regular reports and technical insights to internal teams on the effectiveness and improvements of developed tools.
• Define KPIs for tools and work across various stakeholders to improve it over time.

What we need to see:

• BS, MS, or PhD in EE/CS or related field of education (or equivalent experience) with 10+ years of experience
• Proven record of having worked in management solutions for large scale clusters in data centers.
• Strong and demonstrable skill in Python
• Experience programming and debugging skills for large scale data centers.
• Experience in SCM (e.g., Git, Perforce) and project management tools like Jira.
• Possess excellent written and oral communication skills, excellent work ethics, a deep sense of teamwork, love to produce quality work and commitment to finish your tasks every single day.
• You are a self-starter who loves to find creative solutions to complicated problems and hands on with coding.

• Drive next generation GPU Server Software manageability workflows for scaling AI infrastructure for Datacenters. This infrastructure includes DGX, HGX or MGX Products. You will be involved in ensuring proper tools are built for managing Server Software and Firmware for data center lifecycle.
• Work with internal and external customers to understand requirements for various tools to improve debuggability, serviceability and runtime of data center firmware and software.
• Contribute to all phases of product development, from product definition, architecture, and design, through implementation, debugging, testing and early customer support.
• Maintain detailed documentation of tool designs, capabilities, and usage guidelines. Provide regular reports and technical insights to internal teams on the effectiveness and improvements of developed tools.
• Define KPIs for tools and work across various stakeholders to improve it over time