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

NVIDIA is a leader in visual and AI computing, seeking to expand its team with innovative minds. The Applied Machine Learning Engineer will work on developing AI-driven software systems for circuit design, focusing on automation algorithms and deep learning models to enhance design processes.

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++
• 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