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# Solutions Architect, AI and HPC Infrastructure

- **Company:** NVIDIA Corporation
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computing Platforms, Cloud Computing, Computer Clusters, Computer Engineering, Data Centers, Distributed Computing Environment, Distributed Systems, Ethernet, Monitoring of Systems, InfiniBand, Machine Learning, Network Configuration and Change Management, Performance Tuning, Remote Direct Memory Access, Software Deployment, Systems Architecture, AI Infrastructure, Graphics Processing Unit (GPU), Computer Networking Systems, High Performance Computing, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Data Center Networking, Kubernetes, Information Technology, Machine Learning Operations, Docker
- **Published:** October 20, 2026
- **Apply:** https://startup.jobs/senior-solutions-architect-ai-and-hpc-infrastructure-2100-nvidia-usa-10525539

## About the Role

* 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.

## Description

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.