Datacenter Resiliency Architect - New College Grad 2025
NVIDIA
On-site 🏢 19 May
Tech & IT Services
California, United States 🇺🇸
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world.
We are now 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:
- Pursuing or recently completed a Master’s or PhD degree in Computer Engineering, Electrical Engineering or closely related degree or equivalent experience.
- Familiarity with GPU and Networking Architectures, Computer Architecture basics (including caches, coherence, buses, direct memory access, etc.); Machine Learning/Deep Learning concepts.
- Proficiency in RAS concepts and 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
NVIDIA’s invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of AI factories, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”. Do you love the challenge of crafting the highest-performance silicon possible? If so, we want to hear from you! Come, join our Accelerated and Resilient Compute Systems team and help build the resilient, highly available, cost-effective computing platform driving our success in this exciting and quickly growing field.
You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.
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