Applied AI Engineer, Silicon Co-Design Group, New College Grad 2026

NVIDIA•Santa Clara, CA

2d•Onsite

About The Position

NVIDIA has continuously reinvented itself over two decades, with the invention of the GPU sparking the growth of PC gaming, redefining modern computer graphics, and revolutionizing parallel computing. More recently, GPU deep learning ignited modern AI, marking the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new, hard-to-solve opportunities that matter to the world. This role is part of the Silicon Co-design Engineering Team, responsible for productizing NVIDIA's chips into groundbreaking consumer, professional, server, mobile, and automotive solutions. The qualified candidate should be comfortable in a lab environment and demonstrate a passion for the creation, execution, and improvement of silicon validation plans.

Requirements

M.S. or Ph.D. (or completing within 6 months) in CS/EE/CE or related field or equivalent experience.
Strong Python programming skills; plus C/C++ and/or Tcl/Perl/Bash.
Understanding of model development and evaluation; familiarity with Transformers/LLMs and at least one of CNN/RNN/GNN concepts.
Hands-on experience with ML frameworks PyTorch / TensorFlow.
Strong fundamentals in Git, code reviews, testing, CI/CD, documentation.
Strong debugging/problem-solving skills, ability to handle ambiguity, and effective communication/collaboration across HW/SW teams.
Interest in applying AI to semiconductor co-design/validation problems and learning the domain quickly.

Nice To Haves

Familiarity with statistical methods, tools for data analysis, and analyzing large datasets to draw actionable conclusions, possibly applying deep learning techniques.
Knowledgeable in signal integrity, timing analysis, fault analysis, sampling, computer architecture, filters.
Familiar with lab tools (oscilloscopes and logic analyzers).
Experience in Database and Web Development.

Responsibilities

Build and deploy AI/ML + GenAI solutions (LLMs, classical ML) to accelerate silicon co-design and validation workflows.
Develop AI assistants and agentic systems for SCG engineers using RAG, tool-calling, and fine-tuned models.
Create scalable data + MLOps pipelines to collect/curate chip design & validation data and support training, evaluation, and production deployment.
Partner with cross-functional silicon teams to identify high-impact automation opportunities, integrate solutions into existing flows, and drive measurable improvements in turnaround time and quality.
Prototype and apply modern ML techniques relevant to silicon co-design and share learnings via tech talks/knowledge sharing.