Systems Software Engineer, Kubernetes Scale - DGX Cloud — NVIDIA (ufficio Zurich)
- Location
- Zürich
- Contract
- full-time
- Posted
- 26 days ago
Role overview
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 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!
- Drive end-to-end performance and scale characterization for the NVIDIA DGX Cloud software stack, from Kubernetes control and data planes through NVIDIA components such as GPU Operator, Network Operator, DCGM, NIM, and distributed inference serving, following issues from orchestration down to the metal.
- 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.
Company and context
- 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 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! What you'll be doing:
- Drive end-to-end performance and scale characterization for the NVIDIA DGX Cloud software stack, from Kubernetes control and data planes through NVIDIA components such as GPU Operator, Network Operator, DCGM, NIM, and distributed inference serving, following issues from orchestration down to the metal.
- 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.
Additional details
- 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 Systems
- 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! What you'll be doing:
- 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:
- Experience with performance modeling and benchmarking at scale Proficiency in Golang/Python
- Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI for example) Ways to stand out from the crowd:
- Excellent communication and interpersonal abilities PhD in relevant areas
- If you're creative and autonomous, we want to hear from you!
Notes and original content
- 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 Systems
- 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!
- What you'll be doing:
- 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:
- Experience with performance modeling and benchmarking at scale
- Proficiency in Golang/Python
- Expertise with at least one of public CSP infrastructure (GCP, AWS, Azure, OCI for example)
- Ways to stand out from the crowd:
Questions about this listing
What salary does NVIDIA (ufficio Zurich) offer for this role?
NVIDIA (ufficio Zurich) lists CHF 73'500 - 111'500 gross per year for this position in Zürich. This is the salary published in the original listing (or, when the employer omits a figure, a realistic estimate for the role and sector) — the calculator on this site converts it to your actual net take-home once cross-border tax and social contributions are applied.
Is this a full-time role, and what type of contract does NVIDIA (ufficio Zurich) offer?
This listing is a full-time position. The contract type shown here comes directly from the employer's original posting; always confirm exact hours, notice period and probation length with NVIDIA (ufficio Zurich) during the application process, since these details can vary by role even within the same contract category.
Do I need a cross-border work permit for a role in Zürich?
EU/EFTA residents living in the border zone of the country adjoining Canton Zürich can apply for a G permit; the Swiss employer files it with that canton's migration office after the contract is signed. Border-zone rules and processing times vary by neighbouring country and canton, so confirm the specifics with Zürich's cantonal migration office or with HR during the application.
How do I apply for this position at NVIDIA (ufficio Zurich)?
Use the "Apply now" button on this page — it links directly to NVIDIA (ufficio Zurich)'s original listing at nvidia.wd5.myworkdayjobs.com, so your application goes straight to the employer's own applicant-tracking system. Frontaliere Ticino does not collect or forward applications itself.
