Nvidia Senior Datacenter Resiliency Architect 

United States, California 

281123046

NVIDIA is seeking a Resiliency Architect to support the development and validation of GPU (graphical processing units) hardware and software resiliency features. In this role, you will be a key member of a team of innovators, challenging the status quo and pushing beyond boundaries. You will have the opportunity to impact the industry's leading Datacenter GPUs and SOCs powering product lines for the growing field of artificial intelligence (AI) and high-performance computing (HPC).

What you'll be doing:

• Architect hardware and software Resiliency features to improve system Reliability, Availability, Serviceability (RAS), and performance in the Datacenter.

• Model and analyze RAS metrics like Failures in Time for permanent and transient errors, and Availability from GPU to Rack to Datacenter. Use models to identify gaps and drive RAS improvements.

• Collaborate with architects, unit designers and software engineers to ensure alignment of verification requirements.

• Develop and implement comprehensive architecture verification testplans for resiliency features

• Execute Architecture Testplan by developing test content, working with Software and Architecture teams to enable, run, and debug tests on Architecture models. Support test debug on RTL, emulation, and silicon.

• Run simulations to analyze Architectural Vulnerability Factor and Liveness of on-die memory, flip-flops, and latches.

• Develop CUDA software diagnostics kernels for to run on clusters of NVIDIA GPUs and identify potential hardware issues.

• Develop and automate fault models to simulate various fault types (e.g., transient faults, stuck-at faults) in gate-level netlist, RTL, architectural model, silicon and other environments.

What we need to see:

• Master’s or PhD degree in Computer Engineering, Electrical Engineering or closely related degree or equivalent experience.

• At least 5+ years of relevant experience.

• Familiarity with GPU and Networking Architectures, Computer Architecture basics (including caches, coherence, buses, direct memory access, etc.); Machine Learning/Deep Learning concepts.

• Strong knowledge and industry expertise in either GPU hardware architecture or RAS features or both.

• Proficiency in developing Architecture models.

• Scripting and automation with Python or similar.

• Proficiency in C/C++.

• Excellent interpersonal skills and ability to collaborate with on-site and remote teams.

• Strong debugging and analytical skills.

• Be self-driven and results oriented.

Ways to stand out from the crowd:

• Experience with resiliency and datacenter RAS.

• Proficiency in Verilog/System Verilog RTL simulations and debug. Ability to set up test benches and integrate various components.

• Programming with CUDA