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NVIDIA

Designs the chips and software that power AI

Engagement Tech Lead - Agentic AI

Full-TimeDeadline 12/21/26
$224k - $431.3k/yr+ Equity
Expert
Master's, PhD
Remote in USA+1 moreMore locations: Santa Clara, CA, USA
Remote
Solution EngineeringImplementation & Technical Account Management
Company Historically Provides H1B Sponsorship

About the job

Requirements
  • At least 12 years of experience in technical Product, Engineering, or Solutions roles across enterprise software and production artificial intelligence, including customer- or partner-facing work.
  • A Master's or PhD in Computer Science, Electrical Engineering, or equivalent experience.
  • A strong artificial intelligence, machine learning, and deep learning foundation, with hands-on experience building enterprise-grade generative artificial intelligence systems, including advanced retrieval-augmented generation, multi-agent architectures, and production large language model deployments.
  • Experience leading technical engagements with customers, earning the trust of senior engineers, architects, and executives, and bringing cross-functional teams along.
  • Proficiency in Python, PyTorch, and the modern agent and large language model stack, including LangChain, an inference engine such as vLLM or TensorRT-LLM, vector databases, MCP, and A2A; direct experience with every listed tool is not required.
  • A solid grasp of enterprise deployment, including MLOps or LLMOps, Kubernetes, Docker, security, compliance, and governance.
  • The ability to research, prototype, collaborate across teams, and carry solutions through to production.
Responsibilities
  • Lead technical engagement with a focused set of ISV partners and work closely with their Software Platform Architects to set objectives, timelines, and adoption plans for each account.
  • Design and ship code, methods, and reference architectures that implement retrieval-augmented generation, inference, and multi-agent, long-horizon workflows on NVIDIA's stack and open tools.
  • Lead technical strategy and execution by running the cadence, tracking adoption, and sharing learnings with Product, Engineering, Research, and Solution Architecture teams.
  • Develop breadth across the agentic artificial intelligence lifecycle, with depth in fine-tuning, parameter-efficient fine-tuning, supervised fine-tuning, post-training, reinforcement learning from verifiable rewards, reasoning, advanced retrieval-augmented generation, multi-agent workflows, skills and harness engineering, agent evaluation and observability, and production inference.
  • Represent partner needs and architecture inside NVIDIA to help Product, Engineering, and Research teams shape the roadmap.
  • Develop deep expertise in agentic platform architecture and keep current with the rapidly changing field and technology stack to provide partners with effective guidance.
Desired Qualifications
  • A track record of influencing complex product decisions through trusted partner relationships and translating customer needs into scalable platform improvements.
  • Proactive anticipation of market trends and advocacy for innovation inside and outside the organization, with in-depth knowledge of the ISV and cloud provider landscape.
  • Agility in high-stakes environments and a demonstrated record of delivering successful outcomes through partner collaborations in the high-velocity generative artificial intelligence landscape.
  • The ability to build deep trust with engineers, executives, and multifunctional teams at NVIDIA and partner organizations.

About the company

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • AI demand is still surging: data center sales rose 117% to $89 billion in the quarter ending July 2026.
  • Stock grants are worth more: shares hit a record in October 2026, up 28% for the year.
  • Engineering dominates hiring: 1,722 of NVIDIA's 2,686 job listings in October 2026 were in engineering.

What critics are saying

  • Big buyers are hedging: Meta's February 2026 agreement covers 6 gigawatts of AMD chips.
  • Financing its buyers is getting harder: lenders pushed back on NVIDIA's $500 billion chip-backed loan plan in October 2026.
  • Staff described long hours: Bloomberg's August 2024 interviews reported seven-day weeks, late nights and shouting matches.

What makes NVIDIA unique

  • Rivals must match its software too: over 7.5 million developers use CUDA and other NVIDIA tools to program its chips.
  • The CEO has a broad direct staff: Jensen Huang says over 60 people report to him, without one-on-ones.
  • People rarely leave: NVIDIA's yearly staff turnover is 3.7%, versus a 21.9% average across the tech industry.

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Benefits

  • Company Equity
  • 401(k) Company Match

Growth & Insights and Company News

6 month growth

1%

1 year growth

0%

2 year growth

-1%
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