24 hours ago
Remote, Germany +2 moreMid Level
Responsibilities
- Lead performance and scale characterization across Kubernetes control and data planes, NVIDIA components, distributed inference serving, and cloud platforms.
- Develop automated tests that simulate real user workloads using custom-built and open-source tools and frameworks.
- Investigate, debug, and resolve performance, scalability, reliability, and root-cause issues in large distributed Kubernetes systems.
- Design monitoring, reporting, and analysis tools for performance and scale testing across software, GPU, and CPU resources.
- Build and maintain continuous, always-on performance and scale testing through a modern CI/CD pipeline.
- Document research and results, present findings at internal and external venues, and engage with Kubernetes, CNCF, and NVIDIA open-source communities.
Requirements
- At least 2 years of experience in computer architecture, networking, storage systems, accelerators, or related systems areas.
- Bachelor's or master's degree in engineering, preferably electrical engineering, computer engineering, or computer science, or equivalent experience.
- Expertise in Kubernetes and familiarity with related CNCF projects.
- Experience with large-scale parallel and distributed accelerator-based systems, performance optimization, AI workloads, performance modeling, and benchmarking.
- Proficiency in Golang or Python.
- Experience with the NVIDIA software ecosystem across training and inference domains.
- Expertise with at least one public cloud provider: GCP, AWS, Azure, or OCI.
- Preferred qualifications include Kubernetes operations, scaling clusters to ultra-large node and object counts, open-source community contributions, strong communication skills, and a relevant PhD.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.