Collaborate with AI researchers, developers and customers to develop innovative, automated tests that simulate real user workloads using custom-built and leading open-source tools and frameworks.
Deep dive into performance and scale issues in complex distributed systems, including interactions between Kubernetes and the NVIDIA software stack, to identify and resolve root causes.
Design and develop monitoring, reporting and analysis tools for performance and scale testing across software, GPU and CPU resources.
Triage, debug and root cause issues related to operating Kubernetes clusters at ultra-large scale, ensuring reliability and efficiency.
Build and maintain a high-velocity framework that enables continuous, always-on performance and scale testing via a modern CI/CD pipeline.
Document research, methodologies and results clearly and concisely, and present findings at internal and external venues, including community conferences such as KubeCon and GTC.
Engage efficiently with upstream communities - including Kubernetes, CNCF and NVIDIA open-source projects - to validate performance and scalability of AI workloads early and help shape design and development decisions.
What we need to see:
8+ years of experience Computer Architecture, Networking, Storage systems, Accelerators and Bachelors/Masters in Engineering (preferably, Electrical Engineering, Computer Engineering, or Computer Science) or equivalent experience
Expertise in Kubernetes and familiarity with related CNCF projects
Background in working with large scale parallel and distributed accelerator-based systems
Expertise optimizing performance and AI workloads on large scale systems
Experience with performance modeling and benchmarking at scale
Proficiency in Golang/Python
Background with the NVIDIA software ecosystem in both training and inference domains
Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI for example)
Ways to stand out from the crowd:
Strong operational experience with any one of the Kubernetes distributions
Prior experience scaling Kubernetes clusters to ultra-large node and object counts
Demonstrated history of working in the open-source community
Excellent communication and interpersonal abilities
PhD in relevant areas
#LI-Hybrid
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 .
Applications for this job will be accepted at least until June 14, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
Additional Information
The DGX Cloud organization at NVIDIA brings together cutting-edge hardware and software innovation to deliver industry-leading accelerated computing for the world's most adventurous AI workloads. We're a team of innovative engineers dedicated to solving some of the world's biggest challenges, constantly driving advancements, and impacting millions of lives worldwide!
We are looking for an outstanding Senior Systems Software Engineer with deep experience in distributed systems, open-source technologies such as Kubernetes and containers, and a strong background in systems performance and scalability. The ideal candidate brings broad, end-to-end experience across the stack - from GPU operator and device plugins to distributed inference serving and cloud platforms - along with the technical depth to investigate and address exciting, real-world problems at scale. In this pivotal role, you will take on the challenge of scaling AI infrastructure while optimizing total cost of ownership, driving down cost per token to unlock the next generation of AI innovation and AI factories!
Nvidia · Santa Clara, CA