Join NVIDIA, a leader in transforming computer graphics, PC gaming, and accelerated computing for over 25 years. We are at the forefront of AI, defining the next era of computing where GPUs power intelligent systems. As an NVIDIAN, you'll experience a supportive and diverse environment, empowered to achieve your best work. We are seeking a dedicated Software Engineer to contribute to our advancements in Kubernetes and AI Observability.

Become an integral part of a focused team pioneering innovative methods to streamline the development, deployment, and monitoring of GPU and DPU accelerated applications.

You will be instrumental in enabling GPUs and DPUs as fundamental resources within container orchestrators like Kubernetes.

Join our core group advancing Cloud Native technologies and enhancing NVIDIA accelerators in Kubernetes environments.

Collaborate with diverse engineering teams across NVIDIA to ensure seamless software integration.

Automate and refine build, test, integration, and release pipelines for cloud-native applications.

Effectively manage multiple projects and adapt to shifting priorities with agility.

A Bachelor's or Master's degree in Computer Science or a related discipline, or equivalent practical experience, is required.

We are looking for candidates with a minimum of 5 years of relevant professional experience.

Expertise in systems programming languages such as Go, coupled with a strong command of Data Structures and Algorithms, is essential.

Demonstrated understanding of Container Orchestration Systems (Kubernetes) and Container Technology is crucial.

In-depth knowledge and hands-on experience with Unix/Unix-like kernel internals, especially Linux, are necessary.

Proven experience in automation using frameworks like Ansible and Terraform, and a track record of setting up and maintaining continuous integration systems are expected.

A robust background in Cloud Computing and Distributed Software design and development is required.

Understanding of performance, security, and reliability principles within complex distributed systems is key.