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

Our work at NVIDIA is dedicated towards a computing model focused on visual and AI computing. We are looking to grow our company and teams with the smartest people in the world and there has never been a more exciting time to join our team!

Our team builds AI-driven software systems for circuit design, combining automation algorithms, DL models, and agentic workflows to accelerate end-to-end design automation.

Responsibilities

• Work within a multi-functional team on projects involving pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI systems for EDA/design automation.
• Work on applications ranging from silicon data analysis, manufacturing process variation analysis, VLSI circuit design, timing, and agent-driven design exploration and agent flow optimization.
• Translate requirements into data science, AI/ML, and agentic system problems; architect and build solutions.
• Test and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools within the organization.
• Analyze datasets, raise and validate hypotheses, extract relevant features, and build models and self-improving workflows on top of them.
• Optimize models, algorithms, and autonomous optimization systems until they reach the desired QOR.

Requirements

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

Ways to Stand Out

• 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.
• Experience with deep learning algorithms, AI agent frameworks, and tools such as PyTorch, LangChain, or LangGraph is a definite plus.