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NVIDIA offers competitive salaries and a generous benefits package.
This information is not specified in the job description.
The role requires 10+ years building production software with a strong focus on containers and Kubernetes, strong Python skills for production-grade tooling/services, expert knowledge of Docker/BuildKit and container technologies, deep experience operating workloads on Kubernetes, and hands-on experience with GPU workloads in Kubernetes.
The role involves collaborating across research, backend, SRE, and product teams, mentoring teammates, and setting high engineering standards for container quality, security, and operability.
Candidates can stand out with expertise in Helm chart design systems, Operators, and platform APIs; experience with OpenAI API, Hugging Face API, and inference backends like vLLM, SGLang, TRT-LLM; background in benchmarking and optimizing inference containers; prior multi-tenant/multi-cluster container delivery experience; or contributions to open-source container, k8s, or GPU ecosystems.
Designs GPUs and AI computing solutions
NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.