Applied Machine Learning Engineer, Circuit Design - New College Grad 2026

NVIDIA in Santa Clara, California, is seeking an Applied Machine Learning Engineer focused on circuit design automation for new graduates entering 2026. This hybrid role sits at the crossroads of AI/ML and hardware design, aiming to accelerate end-to-end design automation across pre-silicon and post-silicon data, with emphasis on SPICE correlation, circuit optimization, and AI-enabled EDA workflows.

Responsibilities

• Collaborate within a multi-functional team on initiatives that involve pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI systems for EDA and design automation.
• Contribute to applications spanning silicon data analysis, manufacturing process variation analysis, VLSI circuit design and timing, and agent-driven design exploration with optimized agent flows.
• Translate requirements into data science, AI/ML, and agentic-system problems; architect and build end-to-end solutions.
• Develop and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools within the organization.
• Analyze datasets, formulate and validate hypotheses, extract relevant features, and build models plus self-improving workflows on top of the data.
• Optimize models, algorithms, and autonomous optimization systems until achieving the desired QOR.

Requirements

• Master's or PhD in Electrical or Computer Engineering, Computer Science, or Applied Mathematics (or equivalent experience).
• Knowledge of circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design is required.
• Prior experience in Applied Math/ML/Software programming with demonstrated ability to code in Python and C++.

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 building agentic systems, autonomous optimization loops, self-improving AI systems, or production-scale AI/ML platforms.
• Familiarity with deep learning algorithms and AI agent frameworks, including PyTorch, LangChain, or LangGraph, is a definite plus.