We are now looking for a Resiliency and Safety Architect for GPU Workloads and Failure Analysis! 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 and Safety Architect to support the development of GPU (graphical processing unit) diagnostics for Resiliency in the Datacenter and Functional Safety in Autonomous Vehicles and Robots. 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 GPUs and SoCs powering product lines ranging from the rapidly growing field of artificial intelligence to self-driving cars and robots.
What you'll be doing:
Characterize real world applications and customer test suites triggering hardware failures in NVIDIA GPUs and other system components that that evade existing hardware and software detection mechanisms. Provide insights to NVIDIA diagnostics developers on the workload behaviors (e.g., execution patterns, memory access, communication, synchronization, concurrency) that stress hardware, to improve effectiveness of our diagnostic test suite, and optimize test time. Workloads span datacenter AI and High-Performance Computing applications, as well as autonomous vehicle and industrial robotics safety.
Study silent data corruption, intermittent faults, and hard-to-reproduce failures in the field, including customer returns (RMAs), to establish root causes, and improve detection by diagnostics.
Design, develop, and validate CUDA software diagnostics kernels to run on Datacenter NVIDIA GPUs and Safety SOCs and identify potential hardware issues.
Collaborate with GPU and system architects, software teams to translate workload insights into new resiliency features
Develop and deploy automation and infrastructure for a resiliency and safety debug cluster.
What we need to see:
Master’s or PhD degree in Computer Engineering, Electrical Engineering or closely related degree or equivalent experience.
At least 6+ 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.
Experience characterizing real world applications by identifying key behaviors and drilling down to low level implementation details including concurrency, occupancy, kernel launches, etc.
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:
CUDA Programming
Understanding of GPU hardware architecture and AI workload execution on GPUs
Understanding factors causing silent data corruption in hardware
Familiarity with datacenter resiliency or functional safety.
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 compact diagnostics to ensure resiliency in the datacenter and functional safety in autonomous vehicles and industrial robotics? If so, we want to hear from you! Come, join our Resiliency and Safety Architecture team and help build the real-time, cost-effective computing platforms driving our success in these exciting and rapidly growing fields.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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.Top Skills
What We Do
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 AI — 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, NVIDIA is increasingly known as “the AI computing company.”
