Description
As a Senior Applied Machine Learning Engineer at NVIDIA, you will be part of a multi-functional team working on projects involving pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI systems for EDA/design automation.
Your responsibilities will include:
- Working on applications ranging from silicon data analysis, manufacturing process variation analysis, VLSI circuit design, timing, and agent-driven design exploration and agent flow optimization.
- Translating requirements into data science, AI/ML, and agentic system problems; architecting and building solutions.
- Testing and releasing models and AI systems that integrate with existing machine learning, design automation, and visualization tools within the organization.
- Analyzing datasets, raising and validating hypotheses, extracting relevant features, and building models and self-improving workflows on top of them.
- Optimizing models, algorithms, and autonomous optimization systems until they reach the desired QOR.
We are looking for someone with a strong background in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design, as well as experience in applied math/ML/software programming, preferably with Python and C++.
Experience with deep learning algorithms, AI agent frameworks, and tools such as PyTorch, LangChain, or LangGraph is a plus.
If you have experience building AI systems for EDA, design automation, or circuit design workflows, research or project experience in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems, or effective verbal/written communication and technical presentation skills, we would love to hear from you!