Senior Solutions Architect, First Time Deployment Validation

🕒 Julho 14

🏄 California, Texas, +2 estados a mais – Remoto

🏄 California – Remote 🤠 Texas – Remote ⚔️ Virginia – Remote ☕ Washington – Remote

💵 $148.000 - $235.750 / ano

⏰ Tempo Integral

🟠 Sênior

💻 Engenheiro de Soluções

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

Candidatar-se

Encontrar Vagas Remotas Similares

Configurar Alerta de Email para Novas Vagas

📊 Verifique sua pontuação de currículo para esta vaga

Melhore suas chances de conseguir uma entrevista verificando sua pontuação de currículo antes de se candidatar.

Enviar Currículo

NVIDIA

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 1993

🏥 Saúde

🏭 Manufatura

🤖 Inteligência Artificial

Healthcare • Manufacturing • Artificial Intelligence

A NVIDIA é uma empresa de tecnologia líder, especializada em computação acelerada e inteligência artificial. A companhia é pioneira em avanços em unidades de processamento gráfico (GPUs), computação em nuvem, data centers e realidade virtual, com foco nos setores de games, automotivo, saúde e robótica. As inovações da empresa, como o NVIDIA Omniverse, transformam processos digitais tradicionais ao viabilizar simulações de alta fidelidade e tarefas de renderização. Suas aplicações abrangem diversos setores, desde veículos autônomos com o NVIDIA DRIVE até soluções de saúde com o NVIDIA Clara, além de análises e fluxos de trabalho impulsionados por IA.

Descrição

• Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters. • Ensure configurations align with guidelines for NCCL, collectives, and distributed training frameworks. • Own the execution of key AI/LLM benchmarks, including setup, orchestration, result collection, and analysis. • Investigate and resolve issues when training jobs or benchmarks fail, hang, or underperform. • Build and improve observability for AI factories (metrics, logs, traces, dashboards) to understand workload behavior and system health. • Develop automation (Python, Shell) for running benchmarks, collecting results, and performing regression checks • Examine communication patterns and NCCL usage for AI/LLM workloads, concentrating on collectives such as AllReduce and AllToAll. • Recommend changes to job configuration, parallelism strategies, and cluster settings to improve throughput, latency, and scaling efficiency. • Work closely with hardware, software, networking, datacenter, and product teams to prepare AI factories for customer use. • Contribute to documentation, guidelines, and readiness collateral that support internal collaborators and customer-facing teams.

🎯 Requisitos

• Bachelor’s degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or related field. • More than 6+ years of experience managing Linux-based systems in HPC, distributed systems, or extensive AI/ML settings. • Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, with practical knowledge of NCCL. • Solid grasp of collective communication patterns, particularly AllReduce and AllToAll, and how they are applied in contemporary ML/LLM training. • Familiarity with LLM training and/or inference workflows using frameworks such as PyTorch or TensorFlow. • Proficiency with Python and Shell/Bash for scripting, automation, and tooling. • Experience with benchmarking (crafting, executing, and interpreting performance benchmarks). • Comfortable working with observability data (metrics, logs, dashboards) to troubleshoot and optimize complex distributed workloads. • Strong communication skills and the ability to work effectively with cross-functional teams.

🏖️ Benefícios

• equity • benefits