Applied AI Engineer

The Applied AI Engineer will join NVIDIA's Silicon Co-Design Group to architect, develop, and deploy AI powered solutions that enhance the design and automation toolchain for silicon development. This position is based in California with hybrid work options, offering a salary range of USD 152,000 to 287,500 per year. The role requires a PhD and at least five years of hands-on experience in building and deploying ML/AI systems or data intensive backend services.

Responsibilities

• Develop AI driven validation pipelines to accelerate post-silicon verification, improving speed, intelligence, and scalability across semiconductor environments. This role focuses on building the next generation of capabilities rather than maintaining existing systems.
• Collaborate directly with cross functional engineering teams to identify friction points where AI can add value, then design and deliver the solution. The impact will be felt across teams, products, and silicon generations.
• Evaluate emerging AI frameworks and architectures ahead of mainstream adoption, advocate for those with clear merit, and build a business case for adoption.
• Construct data systems to quantify AI impact, establish clear metrics, close performance gaps, and drive iterative improvements across the organization to convert insights into lasting gains.

Requirements

• PhD, or BS/MS, or an equivalent combination in CS, EE, CE, or related field, with 5+ years of hands-on experience building and deploying ML/AI systems or data-intensive backend services.
• 2+ years of direct Applied AI experience independently owning an AI agent, LLM powered workflow, or intelligent automation system end-to-end—from prototype to production deployment.
• Strong Python skills and proficiency in at least one statically typed language such as C, C++, C#, Java, or Scala.
• Experience working within a silicon development environment, with exposure to chip and system characterization methodologies, process variation, statistical error rates, or advanced timing and power analysis.
• Hands-on experience with silicon bring-up, characterization, or lab debug using standard tools (oscilloscopes, multimeters, logic analyzers).
• Solid EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a good understanding of firmware and driver structures and hardware interaction.
• Proven ability to manage multiple concurrent projects and apply strong problem solving, communication, and teamwork skills.

Technologies

• Python
• C
• C++
• C#
• Java
• Scala
• PyTorch
• TensorFlow
• NeMo Agent Toolkit
• LangChain
• Semantic Kernel
• AutoGen
• CrewAI
• n8n
• Oscilloscopes
• Multimeters
• Logic Analyzers

Benefits

• Equity
• Benefits
• Competitive salaries

Ways to Stand Out

• Experience debugging complex system level issues involving hardware and software interactions, including leadership or ownership in root-cause analysis of silicon or feature level issues.
• Ability to translate innovative AI research into practical, high impact production tools.
• Familiarity with modern AI technologies and methodologies for crafting and launching LLMs.
• Experience building and deploying orchestration agents managing hundreds to thousands of tools.
• Demonstrated experience with deep learning frameworks like PyTorch or TensorFlow, and hands-on work with agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n.