1 day ago
Remote, United Kingdom +5 moreSenior
Responsibilities
- Drive performance and scale characterization across Kubernetes, NVIDIA components, distributed inference serving, and cloud platforms.
- Develop automated tests that simulate real user workloads using custom-built and open-source tools.
- Investigate and resolve performance, scalability, reliability, and root-cause issues in complex distributed systems.
- Build monitoring, reporting, analysis, and continuous performance-testing tools for GPU and CPU resources.
- Operate and troubleshoot Kubernetes clusters at ultra-large scale while improving efficiency and cost per token.
- Document findings and present research internally, externally, and at technical conferences.
- Engage with Kubernetes, CNCF, and NVIDIA open-source communities to influence performance and scalability decisions.
Requirements
- 8+ years of experience in computer architecture, networking, storage systems, accelerators, or related systems engineering.
- 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, modeling, and benchmarking.
- Proficiency in Golang or Python.
- Experience with the NVIDIA software ecosystem across training and inference.
- Expertise with at least one public cloud platform, such as GCP, AWS, Azure, or OCI.
- Strong operational experience with a Kubernetes distribution, ultra-large cluster scaling, or open-source community participation is preferred.
- A PhD in a relevant area and excellent communication and interpersonal abilities are preferred.
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.