Tech Engagement Lead - Model Builder

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

This role focuses on engaging with leading AI model builders to drive the adoption and optimize the performance of NVIDIA's hardware, systems, and software (e.g., GPUs, DGX, CUDA-X, NeMo, TensorRT) within their generative AI workflows, specifically for training and inference. The role involves technical integration, strengthening partnerships, influencing product roadmaps, and showcasing best practices for scalable AI model development pipelines.

What you'd actually do

  1. Engage with senior technical leaders and research teams at AI model builders.
  2. Accelerate the technical integration of NVIDIA's core generative AI technologies.
  3. Support and strengthen technical implementation plans with partner AI engineering and researchers.
  4. Represent the software needs of large model builders to internal NVIDIA product and engineering teams.
  5. Share standard methodologies for crafting and optimizing highly scalable generative AI model development pipelines across all stages.

Skills

Required

  • 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.
  • Extensive experience working with or developing platforms that facilitate large-scale AI/ML training and inference workloads.
  • 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.
  • Proven ability to communicate and influence senior leadership across engineering and research leaders at partner organizations.
  • Skilled at connecting with engineers, researchers, executives, and multi-functional teams.

Nice to have

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

What the JD emphasized

  • core technology integration
  • partner collaborations
  • large-scale AI/ML training and inference workloads
  • large model builders operate at scale
  • crucial AI model development and business value
  • AI research collaborations

Other signals

  • NVIDIA's pioneering hardware, systems, and software libraries
  • core development pipelines
  • model architecture optimization
  • training infrastructure investments
  • deployment of robust, scalable generative AI solutions
  • end-to-end generative AI workflows
  • large-scale AI/ML training and inference workloads
  • large model architectures
  • distributed training, and inference orchestration
  • large-scale system performance optimization