Serve as the technical focal point between NVIDIA and CSP firmware/software engineering — ensuring GPU behaviors (error recovery flows, thermal protection, power state transitions) are well-documented and accessible for customer integration
Identify cross-CSP GPU SW/FW issue patterns — common update failures, recovery gaps, and configuration problems — and drive documentation, tooling, and test strategy improvements
What we need to see
15+ years of experience in GPU system software, GPU firmware, or accelerator platform engineering. BS or MS in Computer Science, Electrical Engineering, or related field (or equivalent experience)
Deep understanding of GPU architecture internals: streaming multiprocessors, GEMM execution, compute kernels, memory hierarchy, and how firmware/driver decisions impact GPU compute performance
Understanding of multi-GPU fabric architectures (NVLink, or similar) and how firmware coordinates across multiple GPUs in a rack-scale system
Understanding of GPU firmware architecture: VBIOS, GPU microcontroller firmware, InfoROM, and their interaction with the GPU driver stack
Experience with firmware update lifecycle management at scale: multi-device update sequencing, A/B updates, rollback, staged rollout, emergency recovery
Understanding of GPU error handling and recovery flows — how firmware-level errors propagate through the driver stack to application-visible failures
Experience with GPU health monitoring and telemetry: Xid errors, thermal events, power events, ECC counters, and their significance for firmware/software teams
Customer obsession — genuine passion for simplifying GPU firmware integration for fleet-scale customers. Proven success influencing engineering teams to improve quality and fleet manageability
Ways to stand out from the crowd
Direct experience with NVIDIA GPU VBIOS, GPU microcontroller firmware, or GPU driver internals
Background in GPU fleet management at 10K+ GPU scale — firmware rollout, health-based remediation, fleet-wide configuration management
Experience with GPU error taxonomy (Xid classification, NVLink error counters, ECC events) and building runbooks around GPU firmware behavior
Understanding of GPU security: secure boot chain, code signing, attestation, debug authentication, multi-tenancy isolation at the firmware level
Familiarity with GPU power management architecture and its impact on workload performance at fleet scale
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, hardworking and self-motivated, we want to hear from you!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 272,000 USD – 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
