Senior Software System Architect, AI Factory Management
Overview
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NVIDIA is looking for a Senior Software System Architect to join the team shaping networking management systems, AIOps platforms, and applied AI solutions for AI cluster operations. You will work with customers, internal operational teams, fellow architects, Product, and Engineering teams to translate sophisticated operational challenges into clear requirements and scalable architectures, and carry these capabilities through implementation and delivery into products that deliver measurable value for customers.
We seek someone with deep technical judgment, a broad system perspective, and the persistence to move ideas into production—someone who asks sharp questions, catches consequential details, and builds alignment across teams.
What you’ll be doing:
• Define architectures for management and intelligent automation capabilities spanning telemetry, observability, remediation, and operational automation.
• Engage customers, cluster operations teams, and compute authorities to understand their challenges and architect solutions that improve operational efficiency, reliability, and scalability.
• Partner with Product and Engineering to translate architectural recommendations into prioritized features and drive their inclusion in the Plan of Record (POR), defining requirements, dependencies, and success criteria.
• Resolve system tradeoffs across hardware, firmware, software, and data platforms, with attention to scale, reliability, performance, and usability.
• Guide design decisions through implementation, with hands-on development of targeted proofs of concept (PoCs) and technical investigations as needed to validate critical assumptions.
• Offer strategic direction and system-level leadership throughout teams, guiding engineering choices and promoting sophisticated, AI-centered operating practices.
What we need to see:
• MSc. or higher in Computer Engineering or Computer Science (or equivalent experience).
• 8+ years in system or software architecture, with a record of delivering sophisticated distributed systems in production.
• Hands-on experience architecting and delivering distributed software systems, including cloud-native services, deployment, and orchestration.
• Deep understanding of data center infrastructure across compute, networking, and management systems, including how these components interact at scale.
• Ability to connect the big picture with precise technical reasoning—and recognize which details matter.
• Experience translating customer needs into system capabilities and influencing decisions across technical and product teams.
• Strong ownership, initiative, and follow-through, supported by clear technical writing and communication.
Ways to stand out from the crowd:
• Experience with AI factory infrastructure, large-scale GPU clusters, HPC, or distributed AI training and inference workloads.
• Hardware/software co-design experience spanning compute, networking, firmware, and software.
• Deep understanding of telemetry and observability platforms, including applying AI/ML.
• Hands-on development of LLM agents, including orchestration, tool integration, context management, and evaluation.
• Contributions to open-source communities, particularly in AI infrastructure or observability.
With competitive salaries and a generous benefits package, NVIDIA is widely regarded as one of the technology world’s most desirable employers. We offer the opportunity to work with some of the most forward‑thinking engineers in the industry, and due to unprecedented growth, our engineering teams continue to expand. If you are a senior technical leader passionate about intelligent systems, AIOps, and large‑scale AI Factories, we want to hear from you.
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Original posting: jobs.nvidia.com
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NVIDIA Corporation in the RISC-V ecosystem
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Founded in 1993, NVIDIA is an AI computing company. The company is headquartered in Santa Clara, California.
- NVIDIA is bringing CUDA platform support to RISC-V, i.e. allowing a RISC-V CPU to host CUDA GPU applications
- Publicly credited the RVA23 profile as the enabler: "We wouldn't have considered porting CUDA to RISC-V without it"
- Active in RISC-V International profile/standards work and a frequent presence in RISC-V ecosystem coverage
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