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
You will also be eligible for equity and benefits.
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.Skills Required
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related field (or equivalent experience)
- 8+ years hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, HPC, 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, and network 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 large GPU clusters, multi-node training environments, and 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 or HPL
- Applications and systems-level knowledge of OpenMPI, NCCL, and distributed training frameworks and GPU communication patterns
- Experience delivering technical training, workshops, whitepapers, blogs, or mentoring engineers and customers on AI/HPC infrastructure
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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