Full-Time

Tech Engagement Lead

Model Builder

Posted on 5/16/2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$184k - $356.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Santa Clara, CA, USA

In Person

Category
Sales & Solution Engineering (1)
Required Skills
CUDA
Pytorch
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
  • Extensive experience with 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 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
  • Experience communicating with and influencing senior leadership across engineering and research leaders at partner organizations
  • Proven ability to navigate fast-paced environments and drive results in AI research collaborations
  • Skilled at connecting with engineers, researchers, executives, and multi-functional teams
Responsibilities
  • Lead Technical Engagement with senior technical leaders and research teams at AI model builders to optimize end-to-end generative AI workflows using NVIDIA's stack
  • Drive the technical integration of NVIDIA core generative AI technologies into training and inference pipelines of large model builders, including NVIDIA GPU architectures, DGX systems, InfiniBand networking, CUDA-X libraries, NeMo frameworks, and TensorRT
  • Strengthen technical implementation plans with partner AI engineering and researchers, defining objectives, performance breakthroughs, and timelines aligned with model development goals and NVIDIA's AI strategy
  • Represent the software needs of large model builders to internal NVIDIA product and engineering teams and contribute to product roadmap decisions by synthesizing findings from large-scale model training and inference environments
  • Maintain strategic relationships through regular cadence meetings, documenting insights, tracking progress, and providing internal reporting on adoption and impact
  • Showcase best practices for building and optimizing scalable generative AI model development pipelines across stages for large model development
  • Stay updated with the latest NVIDIA hardware, libraries, and system updates and proactively share relevant insights and optimizations with partner model development teams
Desired Qualifications
  • 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 relationships with model builders and understanding their needs
  • Strategic curiosity and drive, anticipating market trends in AI, shaping NVIDIA's roadmap, and championing innovation; understanding the large model builder landscape
  • Act as a technical advocate for NVIDIA GPU systems and software stack within assigned large model builder partners, showcasing capability and value
  • Understanding of large-scale system performance optimization, container orchestration (Kubernetes), and Cloud Native technologies for AI workloads

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Simplify Jobs

Simplify's Take

What believers are saying

  • SAP integration makes Nvidia the default governance layer for enterprise AI agents.
  • Hyperscaler capex from Alphabet and others sustains demand for Blackwell and Vera Rubin.
  • NemoClaw and Nemotron expand Nvidia into edge AI and multimodal software.

What critics are saying

  • Blackwell rental prices fell 30%, signaling weakening pricing power in data centers.
  • China export restrictions exclude $12.5 billion from Q1 FY2027 guidance immediately.
  • Tencent and Alibaba are building domestic chips that directly replace Nvidia demand.

What makes NVIDIA unique

  • CUDA locks developers into Nvidia's software ecosystem across AI and HPC.
  • Blackwell and Grace Blackwell integrate chips, systems, and software for full-stack AI.
  • Networking attach rates strengthen Nvidia's platform beyond standalone GPU sales.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

-1%

1 year growth

-3%

2 year growth

-2%
The Associated Press
Apr 15th, 2026
Matlantis integrates NVIDIA ALCHEMI Toolkit for 10x faster materials simulation

Matlantis has integrated NVIDIA's ALCHEMI Toolkit into its materials simulation platform to accelerate industrial materials discovery. The company previously incorporated NVIDIA Warp-optimised kernels, achieving up to 10x speed improvements in atomistic calculations. The integration includes LightPFP, Matlantis' lightweight potential for large-scale simulations, which uses a server-based architecture with NVIDIA ALCHEMI Toolkit-Ops to reduce communication bottlenecks. Matlantis plans to integrate its flagship Universal Machine-Learning Interatomic Potential with the toolkit to further enhance GPU efficiency. Launched in 2021, Matlantis is a cloud-based atomistic simulator jointly developed by PFN and ENEOS. The platform uses deep learning to increase simulation speeds by tens of thousands of times and serves over 150 companies discovering materials including catalysts, batteries and semiconductors.

CNBC
Apr 14th, 2026
Nvidia stock surges 18% on 10-day winning streak fuelled by $1T GPU orders through 2027

Nvidia shares have climbed 18% over a ten-day winning streak, the longest since 2023. The stock is trading about 8% below its October all-time high of $212.19. CEO Jensen Huang revealed at last month's GTC conference that Nvidia has over $1 trillion in GPU orders through 2027, including Blackwell and next-generation Vera Rubin chips. Data centre revenue surged 75% year-over-year and now comprises 88% of the business, a dramatic shift from five years ago when gaming dominated. The rally follows major deals including Meta's February commitment to deploy millions of Nvidia chips across its global data centres. On Monday, Nvidia denied rumours it was pursuing acquisitions of PC makers Dell or HP. The company also unveiled Ising, a new family of open-source models for quantum computing.

Yahoo Finance
Apr 14th, 2026
D-Wave CEO claims quantum computers could challenge Nvidia's AI dominance with superior power efficiency

D-Wave Quantum CEO Alan Baratz claims quantum computing poses a threat to Nvidia, citing superior energy efficiency. Speaking at the Semafor World Economy Summit, Baratz said D-Wave's quantum computer uses just 10 kilowatts of power—equivalent to five or 10 GPUs—whilst solving problems that would take GPU systems nearly a million years. D-Wave shares rose nearly 16% on Tuesday, part of a 140% gain over the past year. The company reported $2.75 million in Q4 revenue, missing analyst estimates, but bookings surged 471% to $13.4 million. The $5.3 billion company recently secured a $20 million agreement with Florida Atlantic University and acquired Quantum Circuits for $550 million. However, quantum machines remain specialised tools, unable to run large language models that drive Nvidia's dominance.

Yahoo Finance
Apr 14th, 2026
Vertiv partners with Nvidia on AI data centre infrastructure as analysts raise price target to $300

Vertiv Holdings has been reaffirmed with a Buy rating by Evercore ISI, setting a price target of $280, whilst Barclays raised its target from $281 to $300 with an Overweight rating. The electrical equipment company is partnering with Nvidia on AI infrastructure development. On 16th March, Nvidia introduced its Vera Rubin DSX AI Factory reference design, with Vertiv providing critical power and cooling solutions for AI data centres. The partnership integrates Vertiv's infrastructure expertise with Nvidia's AI systems to enhance energy efficiency and performance. Vertiv is developing Vertiv OneCore Rubin DSX, a prefabricated system designed to accelerate AI factory deployment. The Brussels-headquartered company specialises in critical digital infrastructure technologies for data centres and communication networks.

Yahoo Finance
Apr 14th, 2026
Nvidia and Dell: AI infrastructure stocks to buy ahead of May earnings

Nvidia and Dell Technologies are positioned as attractive AI infrastructure investments ahead of their May earnings reports, according to recent analysis. Both companies supply critical hardware for AI computing, with demand for AI capacity continuing to outpace available resources across major cloud services. Nvidia shares have remained flat for six months despite strong fundamentals. Last quarter, its data centre business generated $62 billion in revenue, up 75% year over year, with a 75% gross margin. The company expects over $1 trillion in cumulative orders for its Blackwell and upcoming Rubin chips through 2027. Trading at 17 times next year's expected earnings, Nvidia's valuation appears discounted relative to its 66% revenue growth in fiscal year 2026. Dell Technologies similarly stands to benefit from the AI infrastructure build-out. Both companies report earnings in May.