Software Engineer, AI Infrastructure

AI factories are only as good as the software that runs them. NVIDIA’s DSX OS turns GPU infrastructure into a shared, multi-tenant platform for AI teams, providing scheduling, orchestration, serving, and observability for AI workloads at data-center scale—from training and inference to reinforcement learning and agentic AI.The DSX OS Platform Software group builds that layer through projects such as KAI-Scheduler, Snapshot, Grove, and Run:ai. Our work ships as open source and upstream contributions across the cloud-native and AI serving ecosystems. We focus on performance, reliability at scale, and giving users the insight they need to understand and improve how their workloads run. We are looking for highly skilled software engineers to help shape the future of AI infrastructure. As a Software Engineer in the team, you will design and build scalable, high-performance systems, tackle complex distributed-systems problems, and collaborate with experts across NVIDIA and the broader community to support the next generation of AI workloads. What you’ll be doing: Designing and developing open source platform components with a strong focus on performance, reliability, and scalability. Building and optimizing Kubernetes-native systems: controllers, operators, schedulers, and APIs that orchestrate GPU workloads at scale. Contributing upstream — designing in public, writing proposals, reviewing community code, and engaging with users and maintainers of the projects we work on. Collaborating closely with architects, product managers, and partner teams across NVIDIA to deliver impactful solutions. Writing clean, maintainable, and testable code in Go. Conducting code and design reviews to uphold high-quality standards. What we need to see: B.Sc. in Computer Science or a related field or equivalent experience. 2+ years of experience in backend software development, including system design and architecture. Proficiency in at least one backend programming language. Deep familiarity with Kubernetes and the cloud-native ecosystem. Demonstrated ability to solve complex technical challenges and deliver high-quality solutions. Ways to stand out from the crowd: Expertise in Kubernetes internals and advanced cloud-native technologies. Maintain or contribute to OSS projects, and experience working in a public development process. Familiarity with AI inference workloads, serving frameworks, and performance optimization. Proficiency in Linux, with knowledge in networking, security, storage, and virtualization.