Electrical Design Data Jobs

1 job found

2d ago

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

NVIDIA Santa Clara, CA

Santa Clara, CA

2d ago

$116k - $219k/yr Full time Electrical Engineering Ai Assisted Engineering Artificial Intelligence

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Job Description

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.

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