Tech Engagement Lead - Model Builder at NVIDIA

Santa Clara, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

Requirements

B.S. degree or equivalent experience
7+ years of experience in technical product or engineering roles, with focus on AI/ML, high-performance computing, or distributed systems, emphasizing core technology integration and partner collaborations
Extensive experience working with or developing platforms that facilitate large-scale AI/ML training and inference workloads, including distributed systems, data infrastructure, and 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)
Understanding of techniques for 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
Specific understanding of how large model builders operate at scale
Proven ability to communicate and influence senior leadership across engineering and research leaders at partners

Responsibilities

Lead technical engagement with senior technical leaders and research teams at AI model builders, optimizing their workflows using NVIDIA's complete stack for end-to-end generative AI workflows, and serve as primary technical point of contact
Drive integration of NVIDIA's core generative AI technologies (e.g., GPU architectures, DGX systems, InfiniBand, CUDA-X libraries, NeMo frameworks, TensorRT) into training and inference pipelines of large model builders
Strengthen partnerships by supporting technical implementation plans with partner AI engineering and researchers, defining technical objectives, performance breakthroughs, timelines, and aligning with long-term model development goals and NVIDIA's AI strategy
Influence product roadmaps by representing software needs of large model builders to internal NVIDIA product and engineering teams, synthesizing findings from large-scale model training and inference, identifying cross-industry patterns, and advocating for improvements
Maintain strategic relationships through regular cadence meetings, documenting insights, tracking progress, and providing consistent internal reporting on adoption and impact of NVIDIA technologies
Showcase best practices for crafting and optimizing scalable generative AI model development pipelines across all stages, focused on large model development
Stay updated with latest NVIDIA hardware, libraries, and system updates, and proactively share relevant insights and optimizations with partner model development teams

Skills

Key technologies and capabilities for this role

Generative AINVIDIA GPUDGX SystemsInfiniBandCUDA-XNeMoTensorRTAI Training PipelinesModel Optimization

Questions & Answers

Common questions about this position

What education and experience are required for this role?

A B.S. degree or equivalent experience is required, along with 7+ years of experience in technical product or engineering roles focused on AI/ML, high-performance computing, or distributed systems.

Is this a remote position or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this position?

This information is not specified in the job description.

What are the main responsibilities of the Tech Engagement Lead?

The role involves leading technical engagement with AI model builders, driving integration of NVIDIA's GPU architectures, DGX systems, CUDA-X libraries, and NeMo frameworks into their pipelines, strengthening partnerships, influencing product roadmaps, and showcasing best practices for scalable generative AI model development.

What kind of experience makes a strong candidate for this position?

Candidates with 7+ years in technical product or engineering roles in AI/ML, high-performance computing, or distributed systems, plus the ability to engage senior leaders and drive technical integrations, will stand out.

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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