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What you’ll be doing:
We are designing and architecting a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers.
Design, develop, test, debug, and optimize creative solutions for Datacenter firmware throughout lifecycle.
Work closely with hardware, software, infrastructure, and business teams to transform new firmware features from idea to reality.
Define server-level reliability, availability, and serviceability requirements in collaboration with various customers like CSPs and deliver fault resilient solution at scale as per customer expectations.
Collaborate with hardware, software and firmware teams to drive failure analysis and large scale solution deployment.
Work with engineering teams across NVIDIA to ensure your software integrates seamlessly from the hardware all the way up to the AI training applications.
What we need to see:
BS, MS, or PhD in EE/CS or related field of education (or equivalent experience) with 6+ years of experience active development using Python as primary programming language using Linux as OS.
Highly motivated with strong communication skills, you have the ability to work successfully with multi-functional teams, principles and architects and coordinate effectively across organizational boundaries and geographies.
Familiarity with industry standards and specifications such as SPI, I2C, PCIe, UEFI and PLDM.
System knowledge - how platform management works - areas like BMC-BIOS communication, thermal management, power management, firmware update, device monitoring, firmware security, etc.
Expert level knowledge of a systems programming language (Go, Python) and a solid understanding of Data Structure and Algorithms.
Understanding of performance, security and reliability in complex distributed systems. Familiarity with system level architecture, data synchronization, fault tolerance and state management.
Ways to stand out from the crowd:
Background with In-depth understanding of the interaction of machine check architecture and error flows with system firmware/software.
Familiar with Linux server design, x86/ARM system architecture, interconnects like PCI, and other I/O buses.
Proven operational excellence in designing and maintaining cloud AI infrastructure. Proficiency in architecting and running large-scale distributed systems, independent of cloud providers.
You will also be eligible for equity and .
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