Applied AI Engineer

Job Title

Applied AI Engineer

Role Summary

The Applied AI Engineer will design, build, and deploy AI-driven solutions to improve NVIDIA's silicon development and validation toolchain. You will work with cross-functional engineering teams to integrate LLM-powered workflows, automation agents, and data systems that increase efficiency and scalability across chip design, validation, and bring-up.

Experience Level

Mid-level. The role expects around 5+ years of hands-on experience building and deploying ML/AI systems or data-intensive backend services; 2+ years of direct applied AI ownership of LLM-powered workflows or intelligent automation systems.

Responsibilities

Deliver production AI systems that accelerate silicon development, validation, and automation across teams.

• Architect and implement LLM-powered validation pipelines to speed post-silicon validation and scale testing workflows.
• Integrate AI solutions with cross-functional engineering teams to remove friction and automate processes end-to-end.
• Evaluate emerging AI frameworks and architectures; recommend and prototype technologies for production use.
• Measure impact with data systems and quantitative indicators; iterate to close performance gaps.
• Drive projects from prototype to production, ensuring reliability, scalability, and maintainability.

Requirements

Must-haves for successful performance in this role.

• 5+ years building and deploying ML/AI systems or data-intensive backend services.
• 2+ years owning an applied AI project (AI agent, LLM workflow, or intelligent automation) end-to-end from prototype to production.
• Strong Python skills and proficiency in at least one static language (C, C++, C#, Java, or Scala).
• Experience in a silicon development environment (chip/system characterization, process variation, timing/power analysis).
• Hands-on lab experience with bring-up, characterization, or debug tools (oscilloscopes, multimeters, logic analyzers).
• Solid electrical engineering fundamentals: computer architecture, high-speed interfaces, timing, power, and firmware/driver interactions with hardware.
• Demonstrated ability to manage multiple projects, communicate clearly, and work effectively on cross-disciplinary teams.

Nice-to-have:

• Experience debugging complex HW/SW interactions and leading root-cause analysis for silicon or system-level issues.
• Familiarity translating AI research into production tools and modern LLM development/deployment methods.
• Experience with orchestration agents at scale and frameworks such as PyTorch or TensorFlow.
• Hands-on experience with agentic/orchestration tools (NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, n8n, etc.).

Education Requirements

BS, MS, or PhD (or equivalent practical experience) in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.