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NVIDIA

Tech Engagement Lead - Model Builder

NVIDIA is hiring a Tech Engagement Lead - Model Builder in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions PyTorch, JAX and AI research among its tools and skills.
  • Level classes it as a lead role.
  • NVIDIA has 2,700+ other open roles on Level.

AI in this role

PyTorchJAX
ai-research

NVIDIA is seeking a highly influential Generative AI Technical Engagement Lead to evangelize for, drive, and support the seamless adoption and accelerate the performance of NVIDIA's accelerated computing stack across critical AI model development initiatives with leading AI model builders. As a key technical driver, you will facilitate deep technical integration and deepen our collaboration around NVIDIA's pioneering hardware, systems, and software libraries within their core development pipelines. This role demands a leader who can operate strategically at the intersection of product vision, advanced technical execution, and high-level customer engagement, directly influencing model architecture optimization, training infrastructure investments, and ensuring the deployment of robust, scalable generative AI solutions that redefine the state-of-the-art.

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.

Join NVIDIA at a crucial time as we pioneer Generative AI growth. We are in the infancy stage of building and scaling our Generative AI business for large model development. This role offers a unique opportunity to join this rapid expansion. NVIDIA's hardware, systems, and software libraries are at the heart of this growth. They empower large model builders to revolutionize their operations with powerful AI capabilities. This is your chance to be a key member of a team that will shape the future of AI model development, working with the world's leading AI research labs and the most innovative technologies. Your contributions will directly impact the trajectory of our Generative AI success, making this an unparalleled opportunity for professional growth and significant impact.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Tech Engagement Lead - Model Builder at NVIDIA rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI ResearchPyTorchJAX

Questions you could be asked

  1. Tell me about a research question you investigated. What did you find?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of JAX that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: AI Research, PyTorch, and JAX. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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