Applied Machine Learning Engineer, Circuit Design - New College Grad 2026
Job Description
Applied machine learning for circuit design automation at NVIDIA, combining AI driven models with agent based workflows to accelerate end-to-end design tasks across pre-silicon and post-silicon data in a hybrid Santa Clara, CA setting.
Responsibilities
- Collaborate within a cross-functional team on projects that leverage pre-silicon and post-silicon hardware design data, focusing on circuit optimization, SPICE correlation, and AI enabled design automation.
- Contribute to applications spanning silicon data analysis, manufacturing variation analysis, VLSI circuit design and timing, along with agent driven design exploration and optimization of agent workflows.
- Translate stakeholder requirements into data science, AI/ML, and agent based system problems; design and implement end-to-end solutions.
- Develop, validate, and deploy models and AI systems that integrate with existing machine learning, design automation, and visualization tools used internally.
- Examine datasets to form hypotheses, extract relevant features, and build models along with self improving workflows that operate on top of the data.
- Refine models, algorithms, and autonomous optimization loops until performance meets the targeted QOR.
Requirements
- Master's or PhD in Electrical or Computer Engineering, Computer Science, or Applied Mathematics, or equivalent practical experience.
- Solid knowledge of circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design.
- Proven experience in applied math, ML, or software development with a track record of Python and C++ coding.
Technologies
- Python
- C++
- PyTorch
- LangChain
- LangGraph
- SPICE
Benefits
- Equity
- Benefits
Ways to Stand Out From the Crowd
- 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.
- Experience constructing agent based systems, autonomous optimization loops, self improving AI systems, or production scale AI/ML platforms.
- Background with deep learning methods and AI agent frameworks, and familiarity with PyTorch, LangChain, or LangGraph is a plus.
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