Note: The job is a remote job and is open to candidates in USA. NVIDIA is a technology company advancing semiconductor and AI innovation through its Silicon Co-Design Group. The Applied AI Engineer will architect, develop, and deploy AI-powered validation pipelines and automation solutions, integrate AI across engineering teams, evaluate emerging technologies, and measure improvements across silicon development workflows.
Responsibilities
- 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
- 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
- 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
- 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
- Strong EE fundamentals, including computer architecture, high-speed interfaces, timing, power basics, and a solid understanding of firmware/driver structures and hardware interaction
- Hands-on experience in production test, system validation, post-silicon bring-up, reliability, silicon debug, or silicon productization areas such as ATE, SLT, board-level test, validation, or yield analysis
- Experience working within a silicon development environment, with exposure to chip and system characterization methodologies; familiarity with manufacturing and quality metrics (e.g., yield, FPY, DPPM, RAS, TTR, escape rate)
- Proven track record in balancing multiple concurrent projects and applying excellent problem-solving, communication, and teamwork skills
- Exposure to GPU, CPU, AI accelerator, networking, automotive, or other large-scale SoC programs
- 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
- Ability to translate innovative AI research into practical, high-impact production 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
- Equity
- Benefits package
- Hybrid work arrangement
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