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NVIDIA

Designs the chips and software that power AI

Tech Engagement Lead - Model Builder

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A B.S. degree or equivalent experience.
  • At least 7 years of experience in technical product or engineering roles focused on AI/ML, high-performance computing, or distributed systems, including core technology integration and partner collaborations.
  • Extensive experience working with or developing platforms that support large-scale AI/ML training and inference workloads, including distributed systems, data infrastructure, and GPU cluster technologies.
  • Hands-on knowledge of large model architectures such as Transformers and Diffusion Models; familiarity with deep learning frameworks such as PyTorch and JAX, and NVIDIA AI acceleration libraries such as CUDA, cuDNN, NCCL, TensorRT, and NeMo; understanding of model customization, distributed training, and inference orchestration.
  • Strong understanding of compute infrastructure environments, including GPU cluster management, high-speed networking, parallel file systems, and deployment across on-premise and cloud infrastructures, as well as how large model builders operate at scale.
  • Proven ability to communicate with and influence senior engineering and research leaders at partner organizations, connecting NVIDIA technology capabilities to AI model development and business value.
  • Ability to act decisively and achieve results in fast-paced environments, particularly AI research collaborations.
  • Ability to connect with engineers, researchers, executives, and multifunctional teams.

Responsibilities

  • Engage with senior technical leaders and research teams at AI model builders, optimize their end-to-end generative AI workflows using NVIDIA's stack, and serve as a primary technical point of contact.
  • Accelerate technical integration of NVIDIA GPU architectures, DGX systems, InfiniBand, CUDA-X libraries, NeMo frameworks, and TensorRT inference libraries into large model builders' training and inference pipelines.
  • Support and strengthen technical implementation plans with partner AI engineers and researchers; define technical objectives, performance goals, and timelines aligned with model development goals and NVIDIA's AI strategy.
  • Represent large model builders' software needs to NVIDIA product and engineering teams, synthesize findings from large-scale training and inference environments, identify cross-industry patterns, and advocate for improvements to NVIDIA technologies.
  • Conduct regular partner cadence meetings, document insights, track progress, and report internally on adoption and impact of NVIDIA technologies.
  • Share methodologies for creating and optimizing scalable generative AI model development pipelines across all stages of large model development.
  • Keep current with NVIDIA hardware, libraries, and system updates, and share relevant insights and optimizations with partner model development teams.
  • Act as a technical advocate for NVIDIA GPU systems and software stack within assigned large model builder partners, showcasing their technical capabilities and value proposition.

Desired Qualifications

  • Hands-on experience with large language models, diffusion models, distributed training frameworks, and advanced optimization techniques, including the ability to prototype quickly and integrate into model development pipelines.
  • Ability to influence complex product and research decisions by building positive relationships and understanding model builder needs.
  • Strategic curiosity to anticipate AI market trends, shape NVIDIA's roadmap, champion innovation, and understand the large model builder landscape.
  • Understanding of large-scale system performance optimization, container orchestration such as Kubernetes, and cloud-native technologies for AI workloads.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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