Note: The job is a remote job and is open to candidates in USA. NVIDIA is a technology company whose Silicon Co-Design Group develops tools and infrastructure for chip design and automation. NVIDIA is seeking an Applied AI Engineer to architect, build, and deploy AI solutions for semiconductor validation and engineering workflows, while integrating AI across teams, evaluating emerging technologies, and measuring business impact.
Responsibilities
- LLM-Powered Validation Pipelines: Design and deploy AI systems that make post-silicon validation faster, smarter, and more scalable across semiconductor environments. You're not maintaining what exists, you're building what comes next
- Cross-Team AI Integration: Work directly with multi-functional engineering teams across the organization to identify where AI can eliminate friction, and then build the solution. Your output will be felt across teams, products, and generations of silicon
- Technology Scouting & Evaluation: Evaluate emerging AI frameworks and architectures before the rest of the industry catches on. Be the person who spots what's worth adopting, and makes the case for it
- Impact Measurement & Continuous Improvement: Build the data systems that prove what's working. Establish clear, quantitative indicators of AI impact, close performance gaps, and drive iteration across the org to turn insight into lasting improvement!
Skills
- BS, MS, or PhD or equivalent experience in CS, EE, CE, or a 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 through production deployment
- Strong Python skills and proficiency in at least one static 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/power analysis
- Hands-on experience with silicon bring-up, characterization, or lab debug using standard tools (e.g., oscilloscopes, multimeters, logic analyzers)
- Strong EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a solid understanding of firmware/driver structures and hardware interaction
- Proven track record to balance multiple concurrent projects and apply excellent problem-solving, communication, and teamwork skills
- Experience debugging complex system-level issues involving HW/SW interactions, including leadership or ownership in driving 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 with building and deploying orchestration agents managing hundreds to thousands of tools
- Demonstrated experience with deep learning frameworks like PyTorch or TensorFlow, and hands-on experience with agentic and orchestration tools including NeMo Agent Toolkit, LangChain, Semantic Kernel, AutoGen, CrewAI, or n8n
Benefits
- You will also be eligible for equity and benefits.
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