2 days ago
Remote, Germany +3 moreSenior
Responsibilities
- Architect and scale high-performance distributed AI infrastructure on-premises or in the cloud using NVIDIA GPU supercomputers.
- Integrate NVIDIA technology into HPC architectures for scientific and engineering applications.
- Support development activities and customer proofs of concept and proofs of value.
- Validate new features and architectures and develop solutions that showcase the AI ecosystem.
- Engage with customers and coordinate complex technology implementations across multiple initiatives.
Requirements
- Bachelor's or master's degree in engineering, mathematics, physics, or computer science, or equivalent experience.
- 5+ years of experience in software development or ML engineering.
- Extensive ability to solve problems within customer infrastructure.
- Practical expertise with on-premises Kubernetes infrastructure, orchestration, and platforms.
- Experience with containers and MLOps tools.
- Experience with modern deep learning software architecture and frameworks including PyTorch, vLLM, and TritonServer.
- Strong analytical, problem-solving, time-management, organizational, presentation, and coordination skills.
- Preferred experience with developer digital platforms, NVIDIA operators, Kubernetes operations and configuration customization, and large-scale multi-node training and inference pipelines.
Tech Stack
Categories
Solutions Engineering
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.