Applied Machine Learning Engineer, AI for VLSI Design

Nvidia

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Employment: Full-time
Posted: 10 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $210k

$139k most similar roles pay here $276k

This listing doesn't post a salary. Most similar roles pay $177,287–$242,962.

Based on 240 similar postings.

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About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1047 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 833 roles with salary data.

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At a glance

TL;DR · Applied Machine Learning Engineer, AI for VLSI Design

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As an Applied Machine Learning Engineer - AI for VLSI Design within the Circuit Solutions Group, you will work in a multi-functional team to develop AI-driven software systems for circuit design. You will build and innovate agentic AI solutions for VLSI design problems by analyzing datasets, validating hypotheses, and developing models and algorithms to achieve desired quality of results. Your daily work involves pre-silicon and post-silicon custom circuit design, circuit/layout optimization, and Spice correlation research to advance electronic design automation. The role requires expertise in combinatorial optimization, agentic AI, large language models, and machine learning for chip design and EDA. You must possess strong skills in algorithms, data structures, applied math, and software programming, with a proven ability to write code in Python and C++. This role addresses the technical challenge of automating end-to-end circuit design workflows.

What does a Machine Learning Engineer earn in California?

Median $238250 from 175 postings across 25 companies.

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Skills

Python C++ Machine Learning Large Language Models Agentic AI EDA VLSI Design Combinatorial Optimization Circuit Design Layout Optimization Spice Correlation Data Structures Algorithms Applied Math CMOS Layout DRC LVS

What you'll do

Develop agentic AI solutions to solve complex VLSI design problems.
Implement automation algorithms and deep learning models for end-to-end circuit design.
Perform research on pre-silicon and post-silicon custom circuit design and related data.
Execute circuit and layout optimization techniques for frontier electronic design automation.
Conduct Spice correlation research to improve EDA tools.
Analyze datasets and validate hypotheses to build models that reach desired Quality of Results (QOR).
Write high-quality code in Python and C++ to implement machine learning and combinatorial optimization algorithms.

What we're looking for

MS with 3+ years of experience or a PhD with 1+ year of experience in Electrical/Computer Engineering is required.
Experience in Combinatorial Optimization, Agentic AI and large language models is a strict requirement.
Experience in Machine Learning for Chip Design & EDA is a strict requirement.
Proficiency in Algorithms, Data Structures, Applied Math, and Software programming is required.
Proven ability to write code in Python and C++ is required.
Prior experience in large-scale EDA software development (preferred).
Prior experience in CMOS layout drawing, including schematic-to-layout translation and DRC/LVS compliance (preferred).
Effective verbal/written communication and technical presentation skills (preferred).

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