Senior Solutions Architect, DL Training

🕒 5 dias atrás

🏄 California, Washington – Remoto

🏄 California – Remote ☕ Washington – Remote

⏰ Tempo Integral

🟠 Sênior

💻 Engenheiro de Soluções

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👻 Score fantasma 11%

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• Aberta há 5 dias.
• As vagas desta empresa costumam ser preenchidas em 36 dias.
• Sem salário divulgado.

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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

• Work with Cloud Service Providers to design, build, and demonstrate scalable AI training solutions using NVIDIA hardware and software • Lead technical engagements across foundation model pre-training and post-training, helping customers move from architecture and proofs of concept to production adoption • Develop and optimize end-to-end training workflows spanning data preparation, distributed training, checkpointing, and evaluation, including reinforcement learning pipelines • Build customer-specific proofs of concept addressing critical technical and business needs • Help customers diagnose and resolve bottlenecks across GPU compute, memory, communication, networking, storage, and workload orchestration • Partner with Sales Account Managers and Developer Relations Managers to identify opportunities and drive adoption of NVIDIA platforms for AI training • Prepare and deliver technical presentations, workshops, and architecture reviews for researchers, engineers, technical leaders, and executives • Establish close technical relationships with customers, lead roadmap and architecture discussions, and collaborate with NVIDIA engineering and product teams to resolve issues and inform future capabilities

🎯 Requisitos

• BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Statistics, Physics, or a related technical field, or equivalent experience • 5+ years of relevant professional experience in solutions architecture, software engineering, ML engineering, research engineering, or related technical roles • 3+ years of hands-on experience with large-scale AI systems or distributed training • Strong programming and debugging skills in Python • Hands-on experience developing and optimizing deep learning workloads using PyTorch • Practical experience with foundation model pre-training or post-training • Strong understanding of the broader model development lifecycle • Demonstrated ownership of an end-to-end training workflow, including data preparation, training configuration, execution, evaluation, and troubleshooting • Experience with multi-GPU and multi-node training, including distributed parallelism, mixed precision, memory optimization, and checkpointing • Track record of deploying GPU workloads in cloud computing environments such as AWS, GCP, or Azure • Knowledge of containers and workload orchestration • Ability to lead technical engagements, solve complex problems, and communicate technical ideas and tradeoffs clearly to technical and non-technical audiences • Hands-on experience spanning both pre-training and post-training of large language or multimodal models, including supervised fine-tuning and reinforcement learning • Experience with reinforcement learning from human feedback or verifiable rewards, including reward design, rollout generation, policy optimization, and training stability • Experience with NVIDIA NeMo Framework, Megatron Core, Megatron Bridge, PyTorch FSDP, DeepSpeed, or comparable distributed training technologies • System-level understanding of GPU platforms • Experience profiling and optimizing training workloads

🏖️ Benefícios

• Equity • Benefits • Occasional travel for on-site customer engagements and industry events

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