Senior Software Architect, GPU Networking

NVIDIA's GPU Networking Software Architecture team is looking for a Senior Software Architect to help shape software for next-generation AI infrastructure. In this role, you will combine hands-on software architecture and development with system-level thinking.

Responsibilities:

• Contribute to the architecture and prototyping of GPU networking software for large-scale AI infrastructure.
• Analyse requirements, evaluate design alternatives, and document technical decisions and trade-offs.
• Develop proofs of concept to evaluate interfaces, performance, resiliency, and software-hardware interactions.
• Collaborate with GPU, switch, firmware, driver, networking software, product, and architecture teams to move concepts toward implementation.
• Build knowledge of the GPU networking software stack and take increasing ownership of well-defined architectural areas.

Requirements:

• B.Sc. or M.Sc. in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent experience.
• 5+ years of relevant software development experience in Linux environments.
• Strong understanding of software engineering fundamentals, including debugging, testing, data structures, concurrency, and operating-system concepts.
• Solid foundational knowledge of computer networking.
• Ability to understand complex systems, reason about component interfaces and design trade-offs, and communicate technical ideas clearly.
• Curiosity, initiative, collaborative problem-solving skills, and an eagerness to learn from experienced architects and engineers.
• Ability to work effectively with teams across locations and time zones.

Preferred Qualifications:

• Knowledge in one or more of the following: Ethernet and IP datacenter protocols, management and telemetry protocols, GPU interconnects, Linux kernel or driver development, and network operating systems.
• Experience building prototypes or contributing to system-level architecture and design documents.
• Familiarity with AI communication libraries or programming models such as NCCL, UCX, MPI, CUDA, or equivalents.
• Knowledge of Kubernetes, containers, or cloud-native infrastructure.
• Experience analysing performance, reliability, resiliency, or observability in complex software systems.