Role at a glance
- Salary
- $184K – $287.5K/yr
- Location
- 5 Locations, California, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 8+ years of hands-on experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance...
- Education
- BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or...
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Role Summary
NVIDIA is seeking an AI Solutions Architect to help independent software vendors adopt NVIDIA accelerated infrastructure for training, fine-tuning, inference, retrieval, and agentic AI workloads. The role serves as a technical advisor for accelerated systems architecture, GPU and networking systems, cluster design, orchestration, validation, and production deployment for AI data centers.
What You'll Do
- 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...
- Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos and whitepapers
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
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; familiarity with infrastructure requirements for AI workloads; excellent presentation, communication, problem-solving, documentation, and collaboration skills.
Required
- AI infrastructure
- Accelerated computing
- Distributed systems
- Cloud infrastructure
- High-performance computing
- Machine learning platforms
- AI cluster orchestration
- Scheduling
Preferred
- Architecting AI factories, large GPU clusters, multi-node training environments, production inference platforms
- Deploying LLM training, fine-tuning, RAG, and inference workflows on large-scale AI infrastructure
- 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
- Delivering technical training, workshops, whitepapers, blogs, or mentoring engineers, researchers, and customers on AI/HPC infrastructure
Original job description
Content provided by the employer
Original job description
Content provided by the employer
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.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.