Job description

NVIDIA is seeking an outstanding AI Solutions Architect to assist and support energy-sector customers — power generation, oil & gas, transmission and distribution / grid, and renewables — as they build and operate AI infrastructure on NVIDIA's accelerated computing platforms. 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 for the energy industry. This role will focus on helping energy accounts from datacenter to the edge adopt NVIDIA accelerated infrastructure for training, fine-tuning, inference, retrieval, and agentic AI and HPC workloads. This 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, orchestration, validation, and production deployment for AI data centers and edge environments serving energy customers. We need a curious, collaborative, and creative individual to advance these opportunities across AI infrastructure, industrial digital twins, and accelerated computing for the energy sector, and to become a trusted technical advisor bridging our industry, infrastructure, and partner teams. Engaging with internal developers, researchers, data scientists, and senior leaders is a core part of the role and offers exposure to a wide range of partners and challenges. What you'll be doing: • Partner with energy-sector customers, ISVs, and account teams on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance for AI infrastructure. • Advise on the design, build-out, and optimization of accelerated AI infrastructure for energy customers, including large-scale GPU clusters, AI factories, and edge deployments. • Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations for energy-sector AI workloads. • Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance, and workload insight. • Document what you learn and teach others — through targeted samples, whitepapers, blogs, and working through hard problems with customers. • Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos, and whitepapers for energy-sector AI infrastructure. • Strategically partner with flagship customers and industry-specific solution partners targeting our computing platform. • Collaborate primarily through virtual tools, and be empowered to find the best way to make our partners and customers successful. Travel up to 20% for customer meetings may be required. What we need to see: • BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, other Engineering, or related field (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, with 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, and understanding of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration, and performance tuning. • Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, model deployment, storage performance, and cluster reliability. • Working knowledge of one or more energy sectors (power generation, oil & gas, transmission and distribution / grid, or renewables) and their operational and IT environments. • Excellent presentation, communication, problem-solving, documentation, and collaboration skills. • Strong problem structuring and the self-direction to drive ambiguous, multi-stakeholder projects to results. Ways to stand out from the crowd: • Experience architecting AI factories, large GPU clusters, multi-node training environments, or 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. • Comfortable with modern application-deployment practices such as Docker/Containers and Kubernetes. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! 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 for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 24, 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 energy-sector customers, ISVs, and account teams on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and production deployment guidance for AI infrastructure.
Advise on the design, build-out, and optimization of accelerated AI infrastructure for energy customers, including large-scale GPU clusters, AI factories, and edge deployments.
Support infrastructure design across compute, networking, storage, containers, observability, security, power, and data center operations for energy-sector AI workloads.
Drive adoption of systems monitoring, telemetry, and management tools to improve cluster utilization, reliability, performance, and workload insight.
Document what you learn and teach others — through targeted samples, whitepapers, blogs, and working through hard problems with customers.
Build repeatable reference architectures, deployment guides, sizing guidance, benchmark reports, technical playbooks, demos, and whitepapers for energy-sector AI infrastructure.
Strategically partner with flagship customers and industry-specific solution partners targeting our computing platform.
Collaborate primarily through virtual tools, and be empowered to find the best way to make our partners and customers successful. Travel up to 20% for customer meetings may be required.
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, other Engineering, or related field (or equivalent experience).
Familiarity with infrastructure requirements for AI workloads, including distributed training, inference serving, model deployment, storage performance, and cluster reliability.
Working knowledge of one or more energy sectors (power generation, oil & gas, transmission and distribution / grid, or renewables) and their operational and IT environments.
Excellent presentation, communication, problem-solving, documentation, and collaboration skills.
Strong problem structuring and the self-direction to drive ambiguous, multi-stakeholder projects to results.
Experience architecting AI factories, large GPU clusters, multi-node training environments, or 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.
Comfortable with modern application-deployment practices such as Docker/Containers and Kubernetes.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until October 24, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningci/cdllmdockerkubernetes

Platform: eightfold
Effort: medium