Job description

NVIDIA is seeking outstanding AI Solutions Architects to assist and support customers that are building solutions with our newest AI and accelerated computing technologies. At NVIDIA, our solutions architects work across product, engineering, sales, developer relations, business development, and partner teams to help customers design, deploy and optimize AI infrastructure. This role will focus on helping ISVs adopt NVIDIA accelerated infrastructure for training, fine-tuning, inference, retrieval, and agentic AI workloads. This role is an excellent opportunity to work in an interdisciplinary team at NVIDIA! You will serve as a technical advisor for accelerated systems architecture, GPU and networking systems, cluster design, architectures, orchestration, validation, and production deployment for AI data centers. What You Will Be Doing: • Partner with ISVs on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance • Advise on the design, build-out, and optimization of accelerated AI infrastructure, including large-scale clusters • Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations • Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance and workload insight • Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos and whitepapers • Travel up to 20% customer meetings may be required What We Need To See: • BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience) • 8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms • Strong experience designing, deploying, and operating accelerated computing infrastructure at scale • In-depth knowledge of AI cluster orchestration, scheduling, automation and CI/CD deployment pipelines • Understanding of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration or performance tuning • Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, model deployment, storage performance, and cluster reliability • Excellent presentation, communication, problem-solving, documentation, and collaboration skills Ways To Stand Out From The Crowd: • Experience architecting AI factories, large GPU clusters, multi-node training environments, production inference platforms • Experience deploying LLM training, fine-tuning, RAG, and inference workflows on large-scale AI infrastructure • Experience evaluating cluster performance using benchmarks such as MLPerf, HPL, or workload-specific performance tests • Applications and systems-level knowledge of OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns • Experience delivering technical training, workshops, whitepapers, blogs, or mentoring engineers, researchers, and customers on AI/HPC infrastructure Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until July 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Partner with ISVs on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance
Advise on the design, build-out, and optimization of accelerated AI infrastructure, including large-scale clusters
Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations
Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance and workload insight
Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos and whitepapers
Travel up to 20% customer meetings may be required
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms
Strong experience designing, deploying, and operating accelerated computing infrastructure at scale
In-depth knowledge of AI cluster orchestration, scheduling, automation and CI/CD deployment pipelines
Understanding of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration or performance tuning
Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, model deployment, storage performance, and cluster reliability
Excellent presentation, communication, problem-solving, documentation, and collaboration skills
Experience architecting AI factories, large GPU clusters, multi-node training environments, production inference platforms
Experience deploying LLM training, fine-tuning, RAG, and inference workflows on large-scale AI infrastructure
Experience evaluating cluster performance using benchmarks such as MLPerf, HPL, or workload-specific performance tests
Applications and systems-level knowledge of OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns
Experience delivering technical training, workshops, whitepapers, blogs, or mentoring engineers, researchers, and customers on AI/HPC infrastructure
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until July 20, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

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Keywords to mirror

aimachine learningci/cdllm

Platform: eightfold
Effort: medium