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

Applied Machine Learning Engineer, Circuit Design for New College Grad 2026 at NVIDIA in Santa Clara, CA (hybrid) with a salary range of USD 116,000 - 218,500 per year.

Responsibilities

• Collaborate in a cross-functional team on programs spanning pre-silicon and post-silicon hardware design data, circuit optimization, SPICE correlation, and AI powered design automation.
• Contribute to projects across silicon data analysis, manufacturing process variation analysis, VLSI circuit design and timing, and agent driven design exploration with optimized agent flows.
• Translate stakeholder requirements into data science, AI/ML, and agent-based system problems; design architectures and implement end-to-end solutions.
• Develop, validate, and release models and AI systems that integrate with existing machine learning, design automation, and visualization tools.
• Analyze datasets, formulate and validate hypotheses, extract meaningful features, and build models plus self-improving workflows atop them.
• Refine models, algorithms, and autonomous optimization loops until the desired QOR is achieved.

Requirements

• Master’s degree or PhD in Electrical or Computer Engineering, Computer Science, or Applied Mathematics, or equivalent practical experience.
• Strong background in circuit design, VLSI, ASIC, EDA, silicon analysis, or custom circuit design.
• Demonstrated experience at the intersection of applied math, machine learning, and software development, with proficiency in Python and C++.

Technologies

• Python
• C++
• PyTorch
• LangChain
• LangGraph
• SPICE

Benefits

• Equity
• Benefits

Ways to Stand Out

• Experience building AI systems for EDA, design automation, or circuit design workflows.
• Research or project work in AI-driven EDA, circuit optimization, design-space exploration, or autonomous design systems.
• Background creating agent-based systems, autonomous optimization loops, self-improving AI platforms, or production-scale AI/ML pipelines.
• Proficiency with deep learning algorithms and AI agent frameworks; hands-on use of PyTorch, LangChain, or LangGraph is highly beneficial.