Nvidia Tech Engagement Lead - Model Builder 

United States, Texas 

728280775

W​hat You Will Be Doing:

• Lead Technical Engagement: Engage with senior technical leaders and research teams at AI model builders. Optimize their workflows by leveraging NVIDIA's complete stack for their end-to-end generative AI workflows. Serve as a primary technical point of contact.

• Drive Integration: Accelerate the technical integration of NVIDIA's core generative AI technologies. This includes NVIDIA GPU architectures, DGX systems, high-performance networking (InfiniBand), CUDA-X libraries, NeMo frameworks, and inference libraries like TensorRT. Integrate these into the training and inference pipelines of large model builders.

• Strengthen Partnerships: Support and strengthen technical implementation plans with partner AI engineering and researchers. Define clear technical objectives, performance breakthroughs, and timelines. Align these with their long-term model development goals and NVIDIA's AI strategy.

• Influence Product Roadmaps: Represent the software needs of large model builders to internal NVIDIA product and engineering teams. Contribute to product roadmap decisions by synthesizing findings from large-scale model training and inference environments. Identify cross-industry patterns and advocate for improvements to NVIDIA's core technologies.

• Maintain Strategic Relationships: Conduct regular cadence meetings. Document insights, track progress, and provide consistent internal reporting on the adoption and impact of NVIDIA technologies.

• Showcase Best Practices: Share standard methodologies for crafting and optimizing highly scalable generative AI model development pipelines across all stages. Focus on the context of large model development.

• Stay Updated: Keep current with the latest NVIDIA hardware, libraries, and system updates. Proactively share relevant insights and optimizations with partner model development teams.

What We Need To See:

• B.S. degree or equivalent experience.

• 7+ years of experience in technical product or engineering roles. Focus areas include AI/ML, high-performance computing, or distributed systems. Emphasis on core technology integration and partner collaborations is key.

• Extensive experience working with or developing platforms that facilitate large-scale AI/ML training and inference workloads. This includes distributed systems, data infrastructure, and groundbreaking GPU cluster technologies.

• Hands-on knowledge of large model architectures (e.g., Transformers, Diffusion Models). Familiarity with core deep learning frameworks (e.g., PyTorch, JAX), and NVIDIA AI acceleration libraries (e.g., CUDA, cuDNN, NCCL, TensorRT, NeMo). Understand techniques for model customization, distributed training, and inference orchestration.

• Strong understanding of compute infrastructure environments. This includes GPU cluster management, high-speed networking, parallel file systems, and deployment across on-premise and cloud infrastructures. Possess specific understanding of how large model builders operate at scale.

• Proven ability to communicate and influence senior leadership across engineering and research leaders at partner organizations. Link NVIDIA technology capabilities to crucial AI model development and business value.

• Successfully navigated fast-paced environments, taking decisive action to achieve results. Especially valuable in AI research collaborations.

• Skilled at connecting with engineers, researchers, executives, and multi-functional teams.

Ways to Stand Out From The Crowd:

• Hands-on experience with large language models (LLMs), diffusion models, distributed training frameworks, and advanced optimization techniques. Ability to prototype quickly and integrate into model development pipelines.

• Influence complex product and research decisions by nurturing positive relationships and understanding model builder needs.

• Eager drive, strategic curiosity. Anticipate market trends in AI, shape NVIDIA's roadmap, and champion innovation. Understand the large model builder landscape.

• Act as a technical advocate for NVIDIA GPU systems and software stack within assigned large model builder partners. Showcase its technical capabilities and strong value proposition.

• Understanding of large-scale system performance optimization, container orchestration (e.g., Kubernetes), and Cloud Native technologies for AI workloads.